From 46972a0f7d4d5d03250cdfed06117f703ada633f Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Thu, 11 Dec 2025 14:03:04 -0500
Subject: [PATCH 01/56] Initial POC for VLLM Python

Generated-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 pyproject.toml                    |   3 +-
 src/guidellm/backends/__init__.py |  13 +
 src/guidellm/backends/backend.py  |   2 +-
 src/guidellm/backends/vllm.py     | 478 ++++++++++++++++++++++++++++++
 4 files changed, 494 insertions(+), 2 deletions(-)
 create mode 100644 src/guidellm/backends/vllm.py

diff --git a/pyproject.toml b/pyproject.toml
index ed83461ef..dc647e8cb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -74,11 +74,12 @@ dependencies = [
 
 [project.optional-dependencies]
 # Meta Extras
-all = ["guidellm[perf,openai,audio,vision]"]
+all = ["guidellm[perf,openai,audio,vision,vllm]"]
 recommended = ["guidellm[perf,openai]"]
 # Feature Extras
 perf = ["orjson", "msgpack", "msgspec", "uvloop"]
 openai = ["tiktoken>=0.11.0", "blobfile>=3.1.0"]
+vllm = ["vllm>=0.6.0"]
 audio = [
     # Lowest version with full torchcodec support
     "datasets[audio]>=4.1.0",
diff --git a/src/guidellm/backends/__init__.py b/src/guidellm/backends/__init__.py
index 6577fa728..f33c64216 100644
--- a/src/guidellm/backends/__init__.py
+++ b/src/guidellm/backends/__init__.py
@@ -21,6 +21,15 @@
     TextCompletionsResponseHandler,
 )
 
+# Conditionally import VLLM backend if available
+try:
+    from .vllm import VLLMPythonBackend
+
+    HAS_VLLM_BACKEND = True
+except ImportError:
+    VLLMPythonBackend = None  # type: ignore[assignment, misc]
+    HAS_VLLM_BACKEND = False
+
 __all__ = [
     "AudioResponseHandler",
     "Backend",
@@ -31,3 +40,7 @@
     "OpenAIHTTPBackend",
     "TextCompletionsResponseHandler",
 ]
+
+# Conditionally add VLLM backend to exports
+if HAS_VLLM_BACKEND:
+    __all__.append("VLLMPythonBackend")
diff --git a/src/guidellm/backends/backend.py b/src/guidellm/backends/backend.py
index bc3fe37a5..94206d310 100644
--- a/src/guidellm/backends/backend.py
+++ b/src/guidellm/backends/backend.py
@@ -21,7 +21,7 @@
 ]
 
 
-BackendType = Literal["openai_http"]
+BackendType = Literal["openai_http", "vllm_python"]
 
 
 class Backend(
diff --git a/src/guidellm/backends/vllm.py b/src/guidellm/backends/vllm.py
new file mode 100644
index 000000000..a91692c73
--- /dev/null
+++ b/src/guidellm/backends/vllm.py
@@ -0,0 +1,478 @@
+"""
+VLLM Python API backend implementation for GuideLLM.
+
+Provides direct Python API integration with VLLM's LLM class, enabling local
+inference without HTTP overhead. Supports both GPU-accelerated and CPU-only
+inference, streaming and non-streaming responses, and flexible configuration
+through dict-style parameters.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import time
+from collections.abc import AsyncIterator
+from typing import Any
+
+try:
+    from vllm import LLM, SamplingParams
+    from vllm.outputs import RequestOutput
+
+    HAS_VLLM = True
+except ImportError:
+    LLM = None  # type: ignore[assignment, misc]
+    SamplingParams = None  # type: ignore[assignment, misc]
+    RequestOutput = None  # type: ignore[assignment, misc]
+    HAS_VLLM = False
+
+from guidellm.backends.backend import Backend
+from guidellm.schemas import GenerationRequest, GenerationResponse, RequestInfo, UsageMetrics
+
+__all__ = ["VLLMPythonBackend"]
+
+
+def _check_vllm_available() -> None:
+    """Check if vllm is available and raise helpful error if not."""
+    if not HAS_VLLM:
+        raise ImportError(
+            "vllm is not installed. Please install it using 'pip install guidellm[vllm]' "
+            "or 'pip install vllm>=0.6.0'"
+        )
+
+
+@Backend.register("vllm_python")
+class VLLMPythonBackend(Backend):
+    """
+    Python API backend for VLLM inference engine.
+
+    Directly uses VLLM's LLM class for local inference. VLLM automatically uses
+    CUDA if available, otherwise falls back to CPU. Handles request/response
+    conversion between GuideLLM schemas and VLLM's native API, with full support
+    for streaming responses.
+
+    Example:
+    ::
+        backend = VLLMPythonBackend(
+            model="meta-llama/Llama-2-7b-chat-hf",
+            vllm_config={
+                "tensor_parallel_size": 1,
+                "gpu_memory_utilization": 0.9,
+            }
+        )
+
+        await backend.process_startup()
+        async for response, request_info in backend.resolve(request, info):
+            process_response(response)
+        await backend.process_shutdown()
+    """
+
+    # Default VLLM configuration
+    DEFAULT_VLLM_CONFIG: dict[str, Any] = {
+        "tensor_parallel_size": 1,
+        "gpu_memory_utilization": 0.9,
+        "max_model_len": 2048,
+    }
+
+    def __init__(
+        self,
+        model: str,
+        vllm_config: dict[str, Any] | None = None,
+        target: str | None = None,  # Ignored for VLLM Python backend
+    ):
+        """
+        Initialize VLLM Python backend with model and configuration.
+
+        :param model: Model identifier or path for VLLM to load
+        :param vllm_config: Dictionary of VLLM LLM initialization parameters.
+            Merged with defaults. Common parameters include:
+            - tensor_parallel_size: Number of tensor parallel replicas
+            - gpu_memory_utilization: Fraction of GPU memory to use
+            - max_model_len: Maximum sequence length
+            - Any other parameter accepted by vllm.LLM.__init__
+            Note: VLLM automatically uses CUDA if available, CPU otherwise.
+            The 'device' parameter is not supported and will be ignored.
+        :param target: Target URL (ignored for VLLM Python backend, which runs locally)
+        """
+        _check_vllm_available()
+        super().__init__(type_="vllm_python")
+
+        self.model = model
+        self.vllm_config = self._merge_config(vllm_config or {})
+
+        # Runtime state
+        self._in_process = False
+        self._llm: LLM | None = None
+
+    def _merge_config(self, user_config: dict[str, Any]) -> dict[str, Any]:
+        """
+        Merge user configuration with defaults.
+
+        :param user_config: User-provided configuration dictionary
+        :return: Merged configuration with defaults applied
+        """
+        config = self.DEFAULT_VLLM_CONFIG.copy()
+
+        # Merge user config, allowing overrides
+        config.update(user_config)
+
+        # Remove 'device' parameter if present - VLLM doesn't accept it
+        # VLLM automatically uses CUDA if available, CPU otherwise
+        # For CPU-only inference, VLLM may require environment variables or
+        # different configuration (e.g., setting CUDA_VISIBLE_DEVICES="")
+        config.pop("device", None)
+
+        # Ensure model is set in config
+        config["model"] = self.model
+
+        return config
+
+    @property
+    def info(self) -> dict[str, Any]:
+        """
+        Get backend configuration details.
+
+        :return: Dictionary containing backend configuration details
+        """
+        return {
+            "model": self.model,
+            "vllm_config": self.vllm_config,
+            "in_process": self._in_process,
+        }
+
+    async def process_startup(self):
+        """
+        Initialize VLLM LLM instance with configured parameters.
+
+        :raises RuntimeError: If backend is already initialized
+        :raises ImportError: If vllm is not available
+        """
+        _check_vllm_available()
+
+        if self._in_process:
+            raise RuntimeError("Backend already started up for process.")
+
+        # Initialize VLLM LLM instance
+        # Run in executor to avoid blocking the event loop
+        loop = asyncio.get_event_loop()
+        self._llm = await loop.run_in_executor(
+            None, lambda: LLM(**self.vllm_config)  # type: ignore[misc]
+        )
+        self._in_process = True
+
+    async def process_shutdown(self):
+        """
+        Clean up VLLM LLM instance and resources.
+
+        :raises RuntimeError: If backend was not properly initialized
+        """
+        if not self._in_process:
+            raise RuntimeError("Backend not started up for process.")
+
+        # VLLM doesn't have explicit cleanup, but we can clear the reference
+        # and let Python's GC handle it
+        self._llm = None
+        self._in_process = False
+
+    async def validate(self):
+        """
+        Validate backend readiness by attempting a test generation.
+
+        :raises RuntimeError: If backend cannot generate or is not initialized
+        """
+        if self._llm is None:
+            raise RuntimeError("Backend not started up for process.")
+
+        # Perform a minimal test generation to verify the model is working
+        try:
+            test_params = SamplingParams(temperature=0.0, max_tokens=1)  # type: ignore[misc]
+            loop = asyncio.get_event_loop()
+            await loop.run_in_executor(
+                None,
+                lambda: self._llm.generate(["test"], test_params),  # type: ignore[misc]
+            )
+        except Exception as exc:
+            raise RuntimeError(
+                "Backend validation failed. VLLM model could not generate text."
+            ) from exc
+
+    async def available_models(self) -> list[str]:
+        """
+        Get available models from this backend.
+
+        :return: List containing the configured model identifier
+        """
+        return [self.model]
+
+    async def default_model(self) -> str:
+        """
+        Get the default model for this backend.
+
+        :return: Model name or identifier
+        """
+        return self.model
+
+    def _extract_prompt(self, request: GenerationRequest) -> str:
+        """
+        Extract prompt text from generation request.
+
+        :param request: Generation request with body containing prompt data
+        :return: Extracted prompt string
+        :raises ValueError: If prompt cannot be extracted
+        """
+        body = request.arguments.body or {}
+
+        if request.request_type == "text_completions":
+            prompt = body.get("prompt", "")
+            if not prompt:
+                raise ValueError("text_completions request must include 'prompt' in body")
+            return prompt if isinstance(prompt, str) else str(prompt)
+
+        if request.request_type == "chat_completions":
+            messages = body.get("messages", [])
+            if not messages:
+                raise ValueError(
+                    "chat_completions request must include 'messages' in body"
+                )
+
+            # Convert chat messages to prompt format
+            # Simple conversion - join messages with newlines
+            prompt_parts = []
+            for msg in messages:
+                role = msg.get("role", "")
+                content = msg.get("content", "")
+                if content:
+                    prompt_parts.append(f"{role}: {content}")
+
+            return "\n".join(prompt_parts)
+
+        raise ValueError(f"Unsupported request type: {request.request_type}")
+
+    def _create_sampling_params(self, request: GenerationRequest) -> SamplingParams:
+        """
+        Create VLLM SamplingParams from generation request.
+
+        :param request: Generation request with parameters
+        :return: Configured SamplingParams instance
+        """
+        body = request.arguments.body or {}
+
+        # Extract common parameters
+        params = {
+            "temperature": body.get("temperature", 1.0),
+            "top_p": body.get("top_p", 1.0),
+            "max_tokens": body.get("max_tokens", 16),
+            "stop": body.get("stop", None),
+            "frequency_penalty": body.get("frequency_penalty", 0.0),
+            "presence_penalty": body.get("presence_penalty", 0.0),
+        }
+
+        # Remove None values
+        params = {k: v for k, v in params.items() if v is not None}
+
+        return SamplingParams(**params)  # type: ignore[misc]
+
+    def _convert_output_to_response(
+        self, request: GenerationRequest, output: RequestOutput
+    ) -> GenerationResponse:
+        """
+        Convert VLLM RequestOutput to GenerationResponse.
+
+        :param request: Original generation request
+        :param output: VLLM request output
+        :return: Standardized GenerationResponse
+        """
+        # Extract generated text
+        generated_text = ""
+        if output.outputs and len(output.outputs) > 0:
+            generated_text = output.outputs[0].text
+
+        # Extract token counts from metrics if available
+        input_tokens = None
+        output_tokens = None
+
+        if hasattr(output, "metrics") and output.metrics:
+            metrics = output.metrics
+            input_tokens = getattr(metrics, "num_input_tokens", None)
+            output_tokens = getattr(metrics, "num_output_tokens", None)
+
+        # Fallback: estimate from text if metrics not available
+        if input_tokens is None:
+            # Rough estimate: ~4 chars per token
+            input_tokens = len(self._extract_prompt(request)) // 4
+
+        if output_tokens is None:
+            output_tokens = len(generated_text.split()) if generated_text else 0
+
+        return GenerationResponse(
+            request_id=request.request_id,
+            request_args=str(request.arguments.model_dump() if request.arguments else {}),
+            response_id=output.request_id if hasattr(output, "request_id") else None,
+            text=generated_text,
+            input_metrics=UsageMetrics(text_tokens=input_tokens),
+            output_metrics=UsageMetrics(
+                text_tokens=output_tokens,
+                text_words=len(generated_text.split()) if generated_text else 0,
+                text_characters=len(generated_text) if generated_text else 0,
+            ),
+        )
+
+    async def _generate_streaming(
+        self, prompt: str, sampling_params: SamplingParams
+    ) -> AsyncIterator[RequestOutput]:
+        """
+        Generate text with streaming support.
+
+        VLLM's Python API doesn't have native async streaming, so we use
+        a background task to generate and yield results incrementally.
+
+        :param prompt: Input prompt text
+        :param sampling_params: Sampling parameters for generation
+        :yields: RequestOutput objects as they become available
+        """
+        if self._llm is None:
+            raise RuntimeError("Backend not started up for process.")
+
+        # VLLM's generate method is synchronous, so we run it in an executor
+        # For true streaming, we'd need to use VLLM's async API or HTTP server
+        # For now, we generate and simulate streaming by yielding the result
+        loop = asyncio.get_event_loop()
+        outputs = await loop.run_in_executor(
+            None,
+            lambda: self._llm.generate([prompt], sampling_params),  # type: ignore[misc]
+        )
+
+        if outputs and len(outputs) > 0:
+            yield outputs[0]
+
+    async def resolve(  # type: ignore[override]
+        self,
+        request: GenerationRequest,
+        request_info: RequestInfo,
+        history: list[tuple[GenerationRequest, GenerationResponse]] | None = None,
+    ) -> AsyncIterator[tuple[GenerationResponse, RequestInfo]]:
+        """
+        Process generation request and yield progressive responses.
+
+        Handles both streaming and non-streaming requests, converting between
+        GuideLLM schemas and VLLM's native API format.
+
+        :param request: Generation request with content and parameters
+        :param request_info: Request tracking info updated with timing metadata
+        :param history: Conversation history (currently not supported)
+        :raises NotImplementedError: If history is provided
+        :raises RuntimeError: If backend is not initialized
+        :raises ValueError: If request type is unsupported
+        :yields: Tuples of (response, updated_request_info) as generation progresses
+        """
+        if self._llm is None:
+            raise RuntimeError("Backend not started up for process.")
+
+        if history is not None:
+            raise NotImplementedError("Multi-turn requests not yet supported")
+
+        if request.request_type not in ("text_completions", "chat_completions"):
+            raise ValueError(
+                f"Unsupported request type '{request.request_type}'. "
+                "Only 'text_completions' and 'chat_completions' are supported."
+            )
+
+        # Extract prompt and create sampling parameters
+        prompt = self._extract_prompt(request)
+        sampling_params = self._create_sampling_params(request)
+        stream = request.arguments.stream or False
+
+        # Run generation in executor to avoid blocking
+        loop = asyncio.get_event_loop()
+
+        if not stream:
+            # Non-streaming generation
+            request_info.timings.request_start = time.time()
+            outputs = await loop.run_in_executor(
+                None,
+                lambda: self._llm.generate([prompt], sampling_params),  # type: ignore[misc]
+            )
+            request_info.timings.request_end = time.time()
+
+            if outputs and len(outputs) > 0:
+                response = self._convert_output_to_response(request, outputs[0])
+                yield response, request_info
+            return
+
+        # Streaming generation
+        try:
+            request_info.timings.request_start = time.time()
+
+            # For streaming, we generate once and then simulate token-by-token streaming
+            # by yielding progressive responses
+            # Note: VLLM's Python API doesn't support true async streaming,
+            # so we generate once and simulate streaming by yielding word-by-word
+            output = None
+            async for output in self._generate_streaming(prompt, sampling_params):
+                generated_text = ""
+                if output.outputs and len(output.outputs) > 0:
+                    generated_text = output.outputs[0].text
+
+                # Simulate streaming by yielding word-by-word
+                # In a production implementation, you'd use VLLM's actual streaming API
+                words = generated_text.split() if generated_text else []
+                accumulated_text = ""
+
+                for i, word in enumerate(words):
+                    iter_time = time.time()
+
+                    if request_info.timings.first_request_iteration is None:
+                        request_info.timings.first_request_iteration = iter_time
+                    request_info.timings.last_request_iteration = iter_time
+                    request_info.timings.request_iterations += 1
+
+                    accumulated_text += (" " if i > 0 else "") + word
+
+                    if request_info.timings.first_token_iteration is None:
+                        request_info.timings.first_token_iteration = iter_time
+                        request_info.timings.token_iterations = 0
+
+                    request_info.timings.last_token_iteration = iter_time
+                    request_info.timings.token_iterations += 1
+
+                    # Create partial response
+                    partial_response = GenerationResponse(
+                        request_id=request.request_id,
+                        request_args=str(
+                            request.arguments.model_dump()
+                            if request.arguments
+                            else {}
+                        ),
+                        response_id=(
+                            output.request_id
+                            if hasattr(output, "request_id")
+                            else None
+                        ),
+                        text=accumulated_text,
+                        input_metrics=UsageMetrics(),
+                        output_metrics=UsageMetrics(
+                            text_words=len(accumulated_text.split()),
+                            text_characters=len(accumulated_text),
+                        ),
+                    )
+                    yield partial_response, request_info
+
+                    # Small delay to simulate real streaming
+                    await asyncio.sleep(0.01)
+
+                # Break after first output (we only expect one)
+                break
+
+            request_info.timings.request_end = time.time()
+
+            # Yield final complete response with full metrics
+            if output is not None:
+                final_response = self._convert_output_to_response(request, output)
+                yield final_response, request_info
+
+        except asyncio.CancelledError as err:
+            # Yield current result to store iterative results before propagating
+            if output is not None:
+                response = self._convert_output_to_response(request, output)
+                yield response, request_info
+            raise err
+

From cfcb09acb88f9fe8a88a03e22f687298834de01c Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Thu, 11 Dec 2025 14:04:15 -0500
Subject: [PATCH 02/56] Set proper process limit for VLLM python

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/src/guidellm/backends/vllm.py b/src/guidellm/backends/vllm.py
index a91692c73..a4a906ef6 100644
--- a/src/guidellm/backends/vllm.py
+++ b/src/guidellm/backends/vllm.py
@@ -103,6 +103,13 @@ def __init__(
         self._in_process = False
         self._llm: LLM | None = None
 
+    @property
+    def processes_limit(self) -> int | None:
+        """
+        Limits VLLM Python to a single process, since it starts up an entire VLLM engine.
+        """
+        return 1
+
     def _merge_config(self, user_config: dict[str, Any]) -> dict[str, Any]:
         """
         Merge user configuration with defaults.

From 165477f287b324aa3786f1794ee468c438a9277b Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Fri, 12 Dec 2025 12:14:42 -0500
Subject: [PATCH 03/56] Convert to using AsyncLLMEngine for VLLM Python

Assisted-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm.py | 349 ++++++++++++++++++----------------
 1 file changed, 180 insertions(+), 169 deletions(-)

diff --git a/src/guidellm/backends/vllm.py b/src/guidellm/backends/vllm.py
index a4a906ef6..27d9462f2 100644
--- a/src/guidellm/backends/vllm.py
+++ b/src/guidellm/backends/vllm.py
@@ -1,26 +1,30 @@
 """
 VLLM Python API backend implementation for GuideLLM.
 
-Provides direct Python API integration with VLLM's LLM class, enabling local
+Provides direct Python API integration with VLLM's AsyncLLMEngine, enabling local
 inference without HTTP overhead. Supports both GPU-accelerated and CPU-only
-inference, streaming and non-streaming responses, and flexible configuration
-through dict-style parameters.
+inference, true async streaming with token-by-token generation, and flexible
+configuration through dict-style parameters.
 """
 
 from __future__ import annotations
 
 import asyncio
 import time
+import uuid
 from collections.abc import AsyncIterator
 from typing import Any
 
 try:
-    from vllm import LLM, SamplingParams
+    from vllm import SamplingParams
+    from vllm.engine.async_llm_engine import AsyncLLMEngine
+    from vllm.engine.arg_utils import AsyncEngineArgs
     from vllm.outputs import RequestOutput
 
     HAS_VLLM = True
 except ImportError:
-    LLM = None  # type: ignore[assignment, misc]
+    AsyncLLMEngine = None  # type: ignore[assignment, misc]
+    AsyncEngineArgs = None  # type: ignore[assignment, misc]
     SamplingParams = None  # type: ignore[assignment, misc]
     RequestOutput = None  # type: ignore[assignment, misc]
     HAS_VLLM = False
@@ -45,10 +49,10 @@ class VLLMPythonBackend(Backend):
     """
     Python API backend for VLLM inference engine.
 
-    Directly uses VLLM's LLM class for local inference. VLLM automatically uses
-    CUDA if available, otherwise falls back to CPU. Handles request/response
-    conversion between GuideLLM schemas and VLLM's native API, with full support
-    for streaming responses.
+    Directly uses VLLM's AsyncLLMEngine for local async inference. VLLM automatically
+    uses CUDA if available, otherwise falls back to CPU. Handles request/response
+    conversion between GuideLLM schemas and VLLM's native API, with true async
+    streaming support for token-by-token generation.
 
     Example:
     ::
@@ -83,12 +87,12 @@ def __init__(
         Initialize VLLM Python backend with model and configuration.
 
         :param model: Model identifier or path for VLLM to load
-        :param vllm_config: Dictionary of VLLM LLM initialization parameters.
+        :param vllm_config: Dictionary of VLLM AsyncEngineArgs initialization parameters.
             Merged with defaults. Common parameters include:
             - tensor_parallel_size: Number of tensor parallel replicas
             - gpu_memory_utilization: Fraction of GPU memory to use
             - max_model_len: Maximum sequence length
-            - Any other parameter accepted by vllm.LLM.__init__
+            - Any other parameter accepted by vllm.AsyncEngineArgs
             Note: VLLM automatically uses CUDA if available, CPU otherwise.
             The 'device' parameter is not supported and will be ignored.
         :param target: Target URL (ignored for VLLM Python backend, which runs locally)
@@ -101,7 +105,7 @@ def __init__(
 
         # Runtime state
         self._in_process = False
-        self._llm: LLM | None = None
+        self._engine: AsyncLLMEngine | None = None
 
     @property
     def processes_limit(self) -> int | None:
@@ -122,12 +126,6 @@ def _merge_config(self, user_config: dict[str, Any]) -> dict[str, Any]:
         # Merge user config, allowing overrides
         config.update(user_config)
 
-        # Remove 'device' parameter if present - VLLM doesn't accept it
-        # VLLM automatically uses CUDA if available, CPU otherwise
-        # For CPU-only inference, VLLM may require environment variables or
-        # different configuration (e.g., setting CUDA_VISIBLE_DEVICES="")
-        config.pop("device", None)
-
         # Ensure model is set in config
         config["model"] = self.model
 
@@ -144,11 +142,12 @@ def info(self) -> dict[str, Any]:
             "model": self.model,
             "vllm_config": self.vllm_config,
             "in_process": self._in_process,
+            "engine_initialized": self._engine is not None,
         }
 
     async def process_startup(self):
         """
-        Initialize VLLM LLM instance with configured parameters.
+        Initialize VLLM AsyncLLMEngine instance with configured parameters.
 
         :raises RuntimeError: If backend is already initialized
         :raises ImportError: If vllm is not available
@@ -158,26 +157,26 @@ async def process_startup(self):
         if self._in_process:
             raise RuntimeError("Backend already started up for process.")
 
-        # Initialize VLLM LLM instance
-        # Run in executor to avoid blocking the event loop
-        loop = asyncio.get_event_loop()
-        self._llm = await loop.run_in_executor(
-            None, lambda: LLM(**self.vllm_config)  # type: ignore[misc]
-        )
+        # Initialize VLLM AsyncLLMEngine instance
+        # AsyncLLMEngine initialization is async-friendly
+        engine_args = AsyncEngineArgs(**self.vllm_config)  # type: ignore[misc]
+        self._engine = AsyncLLMEngine.from_engine_args(engine_args)  # type: ignore[misc]
         self._in_process = True
 
     async def process_shutdown(self):
         """
-        Clean up VLLM LLM instance and resources.
+        Clean up VLLM AsyncLLMEngine instance and resources.
 
         :raises RuntimeError: If backend was not properly initialized
         """
         if not self._in_process:
             raise RuntimeError("Backend not started up for process.")
 
-        # VLLM doesn't have explicit cleanup, but we can clear the reference
-        # and let Python's GC handle it
-        self._llm = None
+        # Shutdown the async engine if it has a shutdown method
+        if self._engine is not None:
+            self._engine.shutdown()
+            self._in_process = False
+            self._engine = None
         self._in_process = False
 
     async def validate(self):
@@ -186,17 +185,17 @@ async def validate(self):
 
         :raises RuntimeError: If backend cannot generate or is not initialized
         """
-        if self._llm is None:
+        if self._engine is None:
             raise RuntimeError("Backend not started up for process.")
 
         # Perform a minimal test generation to verify the model is working
         try:
             test_params = SamplingParams(temperature=0.0, max_tokens=1)  # type: ignore[misc]
-            loop = asyncio.get_event_loop()
-            await loop.run_in_executor(
-                None,
-                lambda: self._llm.generate(["test"], test_params),  # type: ignore[misc]
-            )
+            request_id = str(uuid.uuid4())
+            # Use async generation for validation
+            async for _ in self._engine.generate("test", test_params, request_id):  # type: ignore[misc]
+                # Just consume the first output to verify it works
+                break
         except Exception as exc:
             raise RuntimeError(
                 "Backend validation failed. VLLM model could not generate text."
@@ -208,6 +207,7 @@ async def available_models(self) -> list[str]:
 
         :return: List containing the configured model identifier
         """
+        # VLLM only supports one model per VLLM instance.
         return [self.model]
 
     async def default_model(self) -> str:
@@ -218,10 +218,40 @@ async def default_model(self) -> str:
         """
         return self.model
 
+    def _extract_text_from_content(self, content: str | list[dict[str, Any]] | Any) -> str:
+        """
+        Extract text content from message content field.
+
+        Handles both string content and list-based multimodal content blocks.
+        For list-based content, extracts text from blocks with type "text" and
+        concatenates them together.
+
+        :param content: Content field which can be a string or list of content blocks
+        :return: Extracted text string
+        """
+        if isinstance(content, str):
+            return content
+        if isinstance(content, list):
+            # Extract text from content blocks with type "text"
+            text_parts = []
+            for block in content:
+                if isinstance(block, dict):
+                    block_type = block.get("type", "")
+                    if block_type == "text":
+                        text = block.get("text", "")
+                        if text:
+                            text_parts.append(text)
+            return "".join(text_parts)
+        # Fallback: convert to string
+        return str(content) if content is not None else ""
+
     def _extract_prompt(self, request: GenerationRequest) -> str:
         """
         Extract prompt text from generation request.
 
+        For chat completions, uses the tokenizer's chat template if available,
+        otherwise falls back to simple formatting.
+
         :param request: Generation request with body containing prompt data
         :return: Extracted prompt string
         :raises ValueError: If prompt cannot be extracted
@@ -241,16 +271,34 @@ def _extract_prompt(self, request: GenerationRequest) -> str:
                     "chat_completions request must include 'messages' in body"
                 )
 
-            # Convert chat messages to prompt format
-            # Simple conversion - join messages with newlines
-            prompt_parts = []
-            for msg in messages:
-                role = msg.get("role", "")
-                content = msg.get("content", "")
-                if content:
-                    prompt_parts.append(f"{role}: {content}")
+            # Try to use tokenizer's chat template if available
+            if self._engine is None:
+                raise RuntimeError("Backend not started up while extracting prompt.")
+            tokenizer = self._engine.tokenizer
 
-            return "\n".join(prompt_parts)
+            # Convert messages to format expected by chat template
+            formatted_messages = []
+            for msg in messages:
+                if isinstance(msg, dict):
+                    raw_content = msg.get("content", "")
+                    # Extract text from content (handles both string and list formats)
+                    text_content = self._extract_text_from_content(raw_content)
+                    formatted_messages.append({
+                        "role": msg.get("role", ""),
+                        "content": text_content,
+                    })
+                else:
+                    formatted_messages.append(msg)
+
+            prompt = tokenizer.apply_chat_template(  # type: ignore[misc]
+                formatted_messages,
+                tokenize=False,
+                add_generation_prompt=True,
+            )
+            if isinstance(prompt, str):
+                return prompt
+            else:
+                raise RuntimeError("Code not setup for state")
 
         raise ValueError(f"Unsupported request type: {request.request_type}")
 
@@ -279,13 +327,14 @@ def _create_sampling_params(self, request: GenerationRequest) -> SamplingParams:
         return SamplingParams(**params)  # type: ignore[misc]
 
     def _convert_output_to_response(
-        self, request: GenerationRequest, output: RequestOutput
+        self, request: GenerationRequest, output: RequestOutput, previous_text: str = ""
     ) -> GenerationResponse:
         """
         Convert VLLM RequestOutput to GenerationResponse.
 
         :param request: Original generation request
         :param output: VLLM request output
+        :param previous_text: Previously accumulated text (for streaming)
         :return: Standardized GenerationResponse
         """
         # Extract generated text
@@ -293,22 +342,18 @@ def _convert_output_to_response(
         if output.outputs and len(output.outputs) > 0:
             generated_text = output.outputs[0].text
 
-        # Extract token counts from metrics if available
-        input_tokens = None
-        output_tokens = None
-
-        if hasattr(output, "metrics") and output.metrics:
-            metrics = output.metrics
-            input_tokens = getattr(metrics, "num_input_tokens", None)
-            output_tokens = getattr(metrics, "num_output_tokens", None)
-
-        # Fallback: estimate from text if metrics not available
-        if input_tokens is None:
-            # Rough estimate: ~4 chars per token
-            input_tokens = len(self._extract_prompt(request)) // 4
+        input_tokens = len(output.prompt_token_ids)
 
+        # Extract output token count from VLLM output
+        output_tokens = None
+        if output.outputs and len(output.outputs) > 0:
+            # Try to get token IDs from the output
+            output_obj = output.outputs[0]
+            if output_obj.token_ids is not None:
+                output_tokens = len(output_obj.token_ids)
+            # TODO: Determine if this is correct logic. Do we need to add multiple outputs together?
         if output_tokens is None:
-            output_tokens = len(generated_text.split()) if generated_text else 0
+            raise RuntimeError("Failed to get output tokens")
 
         return GenerationResponse(
             request_id=request.request_id,
@@ -323,34 +368,6 @@ def _convert_output_to_response(
             ),
         )
 
-    async def _generate_streaming(
-        self, prompt: str, sampling_params: SamplingParams
-    ) -> AsyncIterator[RequestOutput]:
-        """
-        Generate text with streaming support.
-
-        VLLM's Python API doesn't have native async streaming, so we use
-        a background task to generate and yield results incrementally.
-
-        :param prompt: Input prompt text
-        :param sampling_params: Sampling parameters for generation
-        :yields: RequestOutput objects as they become available
-        """
-        if self._llm is None:
-            raise RuntimeError("Backend not started up for process.")
-
-        # VLLM's generate method is synchronous, so we run it in an executor
-        # For true streaming, we'd need to use VLLM's async API or HTTP server
-        # For now, we generate and simulate streaming by yielding the result
-        loop = asyncio.get_event_loop()
-        outputs = await loop.run_in_executor(
-            None,
-            lambda: self._llm.generate([prompt], sampling_params),  # type: ignore[misc]
-        )
-
-        if outputs and len(outputs) > 0:
-            yield outputs[0]
-
     async def resolve(  # type: ignore[override]
         self,
         request: GenerationRequest,
@@ -360,8 +377,10 @@ async def resolve(  # type: ignore[override]
         """
         Process generation request and yield progressive responses.
 
-        Handles both streaming and non-streaming requests, converting between
-        GuideLLM schemas and VLLM's native API format.
+        Handles both streaming and non-streaming requests using AsyncLLMEngine's
+        native async streaming capabilities. For streaming requests, yields
+        token-by-token as they are generated. For non-streaming, yields once
+        with the complete response.
 
         :param request: Generation request with content and parameters
         :param request_info: Request tracking info updated with timing metadata
@@ -371,7 +390,7 @@ async def resolve(  # type: ignore[override]
         :raises ValueError: If request type is unsupported
         :yields: Tuples of (response, updated_request_info) as generation progresses
         """
-        if self._llm is None:
+        if self._engine is None:
             raise RuntimeError("Backend not started up for process.")
 
         if history is not None:
@@ -388,98 +407,90 @@ async def resolve(  # type: ignore[override]
         sampling_params = self._create_sampling_params(request)
         stream = request.arguments.stream or False
 
-        # Run generation in executor to avoid blocking
-        loop = asyncio.get_event_loop()
+        # Generate unique request ID for this generation
+        request_id = str(uuid.uuid4())
 
-        if not stream:
-            # Non-streaming generation
-            request_info.timings.request_start = time.time()
-            outputs = await loop.run_in_executor(
-                None,
-                lambda: self._llm.generate([prompt], sampling_params),  # type: ignore[misc]
-            )
-            request_info.timings.request_end = time.time()
+        request_info.timings.request_start = time.time()
 
-            if outputs and len(outputs) > 0:
-                response = self._convert_output_to_response(request, outputs[0])
-                yield response, request_info
-            return
+        # Track accumulated text for streaming
+        accumulated_text = ""
+        final_output: RequestOutput | None = None
 
-        # Streaming generation
         try:
-            request_info.timings.request_start = time.time()
-
-            # For streaming, we generate once and then simulate token-by-token streaming
-            # by yielding progressive responses
-            # Note: VLLM's Python API doesn't support true async streaming,
-            # so we generate once and simulate streaming by yielding word-by-word
-            output = None
-            async for output in self._generate_streaming(prompt, sampling_params):
-                generated_text = ""
-                if output.outputs and len(output.outputs) > 0:
-                    generated_text = output.outputs[0].text
-
-                # Simulate streaming by yielding word-by-word
-                # In a production implementation, you'd use VLLM's actual streaming API
-                words = generated_text.split() if generated_text else []
-                accumulated_text = ""
-
-                for i, word in enumerate(words):
-                    iter_time = time.time()
-
-                    if request_info.timings.first_request_iteration is None:
-                        request_info.timings.first_request_iteration = iter_time
-                    request_info.timings.last_request_iteration = iter_time
-                    request_info.timings.request_iterations += 1
-
-                    accumulated_text += (" " if i > 0 else "") + word
-
-                    if request_info.timings.first_token_iteration is None:
-                        request_info.timings.first_token_iteration = iter_time
-                        request_info.timings.token_iterations = 0
-
-                    request_info.timings.last_token_iteration = iter_time
-                    request_info.timings.token_iterations += 1
-
-                    # Create partial response
-                    partial_response = GenerationResponse(
-                        request_id=request.request_id,
-                        request_args=str(
-                            request.arguments.model_dump()
-                            if request.arguments
-                            else {}
-                        ),
-                        response_id=(
-                            output.request_id
-                            if hasattr(output, "request_id")
-                            else None
-                        ),
-                        text=accumulated_text,
-                        input_metrics=UsageMetrics(),
-                        output_metrics=UsageMetrics(
-                            text_words=len(accumulated_text.split()),
-                            text_characters=len(accumulated_text),
-                        ),
-                    )
-                    yield partial_response, request_info
 
-                    # Small delay to simulate real streaming
-                    await asyncio.sleep(0.01)
+            # Use AsyncLLMEngine's native async generation
+            async for request_output in self._engine.generate(  # type: ignore[misc]
+                prompt, sampling_params, request_id
+            ):
+                iter_time = time.time()
 
-                # Break after first output (we only expect one)
-                break
+                # Update request iteration timing
+                if request_info.timings.first_request_iteration is None:
+                    request_info.timings.first_request_iteration = iter_time
+                request_info.timings.last_request_iteration = iter_time
+                request_info.timings.request_iterations += 1
+
+                # Extract generated text from output
+                generated_text = ""
+                if request_output.outputs and len(request_output.outputs) > 0:
+                    generated_text = request_output.outputs[0].text
+
+                # For streaming, yield incremental updates
+                if stream:
+                    # Check if this is a new token (text has changed)
+                    if generated_text != accumulated_text:
+                        # Update token iteration timing on first new token
+                        if request_info.timings.first_token_iteration is None:
+                            request_info.timings.first_token_iteration = iter_time
+                            request_info.timings.token_iterations = 0
+
+                        request_info.timings.last_token_iteration = iter_time
+                        request_info.timings.token_iterations += 1
+
+                        # Create partial response with incremental text
+                        partial_response = self._convert_output_to_response(
+                            request, request_output, accumulated_text
+                        )
+                        yield partial_response, request_info
+
+                        accumulated_text = generated_text
+
+                # Keep track of final output for non-streaming or final response
+                final_output = request_output
+
+                # For non-streaming, wait for generation to finish
+                if not stream:
+                    # Check if generation is finished
+                    if (request_output.outputs and len(request_output.outputs) > 0 and
+                        request_output.outputs[0].finish_reason is not None):
+                        break
 
             request_info.timings.request_end = time.time()
 
-            # Yield final complete response with full metrics
-            if output is not None:
-                final_response = self._convert_output_to_response(request, output)
+            # For streaming, yield final complete response with full metrics
+            # For non-streaming, yield the complete response
+            if final_output is not None:
+                if stream:
+                    # Final response with complete metrics
+                    final_response = self._convert_output_to_response(
+                        request, final_output, ""
+                    )
+                else:
+                    # Single complete response for non-streaming
+                    final_response = self._convert_output_to_response(
+                        request, final_output, ""
+                    )
                 yield final_response, request_info
 
         except asyncio.CancelledError as err:
             # Yield current result to store iterative results before propagating
-            if output is not None:
-                response = self._convert_output_to_response(request, output)
+            if final_output is not None:
+                response = self._convert_output_to_response(request, final_output, accumulated_text)
                 yield response, request_info
             raise err
+        except Exception as exc:
+            # Re-raise with context
+            raise RuntimeError(
+                f"Generation failed: {exc}"
+            ) from exc
 

From 2f6c47a89f4b8b84e963bed2dcd022856e6d23ae Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Fri, 12 Dec 2025 16:44:10 -0500
Subject: [PATCH 04/56] Added option to log errors from backends

Generated-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/logger.py           | 2 ++
 src/guidellm/scheduler/worker.py | 7 +++++++
 src/guidellm/settings.py         | 1 +
 3 files changed, 10 insertions(+)

diff --git a/src/guidellm/logger.py b/src/guidellm/logger.py
index da3464f9d..51de22c99 100644
--- a/src/guidellm/logger.py
+++ b/src/guidellm/logger.py
@@ -15,6 +15,8 @@
         (default: guidellm.log if log file level set else none)
     - GUIDELLM__LOGGING__LOG_FILE_LEVEL: Log level for file logging
         (default: INFO if log file set else none).
+    - GUIDELLM__LOGGING__LOG_BACKEND_EXCEPTIONS: Enable logging of backend
+        exceptions with stacktraces (default: false).
 
 If logging isn't responding to the environment variables, run the `guidellm config`
 command to validate that the environment variables match and are being set correctly.
diff --git a/src/guidellm/scheduler/worker.py b/src/guidellm/scheduler/worker.py
index 08072eaf7..922cc8349 100644
--- a/src/guidellm/scheduler/worker.py
+++ b/src/guidellm/scheduler/worker.py
@@ -28,6 +28,7 @@
     HAS_UVLOOP = False
 
 
+from guidellm.logger import logger
 from guidellm.scheduler.schemas import (
     BackendInterface,
     MultiTurnRequestT,
@@ -36,6 +37,7 @@
 )
 from guidellm.scheduler.strategies import SchedulingStrategy
 from guidellm.schemas import RequestInfo
+from guidellm.settings import settings
 from guidellm.utils import (
     InterProcessMessaging,
     wait_for_sync_barrier,
@@ -384,6 +386,11 @@ async def _process_next_request(self, target_start: float):
             if request is not None and request_info is not None:
                 request_info.error = str(exc)
                 request_info.timings.resolve_end = time.time()
+                # Log backend exception if enabled
+                if settings.logging.log_backend_exceptions:
+                    logger.exception(
+                        f"Backend exception for request {request_info.request_id}: {exc}"
+                    )
                 self._send_update("errored", response, request, request_info)
         finally:
             if request_info is not None:
diff --git a/src/guidellm/settings.py b/src/guidellm/settings.py
index b18094c65..90a1e8ec9 100644
--- a/src/guidellm/settings.py
+++ b/src/guidellm/settings.py
@@ -49,6 +49,7 @@ class LoggingSettings(BaseModel):
     console_log_level: str = "WARNING"
     log_file: str | None = None
     log_file_level: str | None = None
+    log_backend_exceptions: bool = False
 
 
 class DatasetSettings(BaseModel):

From c2cc6d38bda48df9a6e930b788045e44fe911ba3 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Fri, 12 Dec 2025 16:50:39 -0500
Subject: [PATCH 05/56] Refactor VLLM Py Backend to use same pattern as OpenAI
 backend

Assisted-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm.py | 313 ++++++++++++++++++++++------------
 1 file changed, 204 insertions(+), 109 deletions(-)

diff --git a/src/guidellm/backends/vllm.py b/src/guidellm/backends/vllm.py
index 27d9462f2..90dc9f1ac 100644
--- a/src/guidellm/backends/vllm.py
+++ b/src/guidellm/backends/vllm.py
@@ -30,7 +30,9 @@
     HAS_VLLM = False
 
 from guidellm.backends.backend import Backend
+from guidellm.backends.response_handlers import GenerationResponseHandlerFactory
 from guidellm.schemas import GenerationRequest, GenerationResponse, RequestInfo, UsageMetrics
+from guidellm.utils import json
 
 __all__ = ["VLLMPythonBackend"]
 
@@ -82,6 +84,7 @@ def __init__(
         model: str,
         vllm_config: dict[str, Any] | None = None,
         target: str | None = None,  # Ignored for VLLM Python backend
+        response_handlers: dict[str, Any] | None = None,
     ):
         """
         Initialize VLLM Python backend with model and configuration.
@@ -175,7 +178,6 @@ async def process_shutdown(self):
         # Shutdown the async engine if it has a shutdown method
         if self._engine is not None:
             self._engine.shutdown()
-            self._in_process = False
             self._engine = None
         self._in_process = False
 
@@ -218,6 +220,69 @@ async def default_model(self) -> str:
         """
         return self.model
 
+    def _validate_backend_initialized(self) -> None:
+        """
+        Validate that the backend is initialized and ready.
+
+        :raises RuntimeError: If backend is not initialized
+        """
+        if self._engine is None:
+            raise RuntimeError("Backend not started up for process.")
+
+    def _validate_history(
+        self, history: list[tuple[GenerationRequest, GenerationResponse]] | None
+    ) -> None:
+        """
+        Validate that history is not provided (not yet supported).
+
+        :param history: Conversation history
+        :raises NotImplementedError: If history is provided
+        """
+        if history is not None:
+            raise NotImplementedError("Multi-turn requests not yet supported")
+
+    def _validate_request_type(self, request_type: str) -> None:
+        """
+        Validate that the request type is supported.
+
+        :param request_type: The request type to validate
+        :raises ValueError: If request type is not supported
+        """
+        if request_type not in ("text_completions", "chat_completions"):
+            raise ValueError(
+                f"Unsupported request type '{request_type}'. "
+                "Only 'text_completions' and 'chat_completions' are supported."
+            )
+
+    def _update_request_timing(self, request_info: RequestInfo, iter_time: float) -> None:
+        """
+        Update request iteration timing information.
+
+        :param request_info: Request tracking info to update
+        :param iter_time: Current iteration time
+        """
+        if request_info.timings.first_request_iteration is None:
+            request_info.timings.first_request_iteration = iter_time
+        request_info.timings.last_request_iteration = iter_time
+        request_info.timings.request_iterations += 1
+
+    def _update_token_timing(
+        self, request_info: RequestInfo, iter_time: float, iterations: int = 1
+    ) -> None:
+        """
+        Update token iteration timing information.
+
+        :param request_info: Request tracking info to update
+        :param iter_time: Current iteration time
+        :param iterations: Number of token iterations (default: 1)
+        """
+        if request_info.timings.first_token_iteration is None:
+            request_info.timings.first_token_iteration = iter_time
+            request_info.timings.token_iterations = 0
+
+        request_info.timings.last_token_iteration = iter_time
+        request_info.timings.token_iterations += iterations
+
     def _extract_text_from_content(self, content: str | list[dict[str, Any]] | Any) -> str:
         """
         Extract text content from message content field.
@@ -326,47 +391,58 @@ def _create_sampling_params(self, request: GenerationRequest) -> SamplingParams:
 
         return SamplingParams(**params)  # type: ignore[misc]
 
-    def _convert_output_to_response(
-        self, request: GenerationRequest, output: RequestOutput, previous_text: str = ""
-    ) -> GenerationResponse:
+    def _convert_vllm_output_to_openai_format(
+        self, request: GenerationRequest, output: RequestOutput
+    ) -> dict[str, Any]:
         """
-        Convert VLLM RequestOutput to GenerationResponse.
+        Convert VLLM RequestOutput to OpenAI-style dict format.
+
+        Converts VLLM's native output format to the format expected by
+        GenerationResponseHandler, matching OpenAI API response structure.
 
         :param request: Original generation request
         :param output: VLLM request output
-        :param previous_text: Previously accumulated text (for streaming)
-        :return: Standardized GenerationResponse
+        :return: OpenAI-style response dictionary
         """
         # Extract generated text
         generated_text = ""
         if output.outputs and len(output.outputs) > 0:
             generated_text = output.outputs[0].text
 
+        # Calculate token counts
         input_tokens = len(output.prompt_token_ids)
-
-        # Extract output token count from VLLM output
-        output_tokens = None
+        output_tokens = 0
         if output.outputs and len(output.outputs) > 0:
-            # Try to get token IDs from the output
             output_obj = output.outputs[0]
             if output_obj.token_ids is not None:
                 output_tokens = len(output_obj.token_ids)
-            # TODO: Determine if this is correct logic. Do we need to add multiple outputs together?
-        if output_tokens is None:
-            raise RuntimeError("Failed to get output tokens")
-
-        return GenerationResponse(
-            request_id=request.request_id,
-            request_args=str(request.arguments.model_dump() if request.arguments else {}),
-            response_id=output.request_id if hasattr(output, "request_id") else None,
-            text=generated_text,
-            input_metrics=UsageMetrics(text_tokens=input_tokens),
-            output_metrics=UsageMetrics(
-                text_tokens=output_tokens,
-                text_words=len(generated_text.split()) if generated_text else 0,
-                text_characters=len(generated_text) if generated_text else 0,
-            ),
-        )
+
+        # Build usage dict
+        usage: dict[str, int] = {
+            "prompt_tokens": input_tokens,
+            "completion_tokens": output_tokens,
+            "total_tokens": input_tokens + output_tokens,
+        }
+
+        # Build choices array based on request type
+        if request.request_type == "text_completions":
+            choices = [{"text": generated_text}]
+        elif request.request_type == "chat_completions":
+            choices = [{"message": {"content": generated_text, "role": "assistant"}}]
+        else:
+            choices = [{"text": generated_text}]
+
+        # Build response dict
+        response_dict: dict[str, Any] = {
+            "choices": choices,
+            "usage": usage,
+        }
+
+        # Add response ID if available
+        if hasattr(output, "request_id") and output.request_id:
+            response_dict["id"] = output.request_id
+
+        return response_dict
 
     async def resolve(  # type: ignore[override]
         self,
@@ -390,17 +466,13 @@ async def resolve(  # type: ignore[override]
         :raises ValueError: If request type is unsupported
         :yields: Tuples of (response, updated_request_info) as generation progresses
         """
-        if self._engine is None:
-            raise RuntimeError("Backend not started up for process.")
+        # Validate request
+        self._validate_backend_initialized()
+        self._validate_history(history)
+        self._validate_request_type(request.request_type)
 
-        if history is not None:
-            raise NotImplementedError("Multi-turn requests not yet supported")
-
-        if request.request_type not in ("text_completions", "chat_completions"):
-            raise ValueError(
-                f"Unsupported request type '{request.request_type}'. "
-                "Only 'text_completions' and 'chat_completions' are supported."
-            )
+        # Create response handler
+        response_handler = GenerationResponseHandlerFactory.create(request.request_type)
 
         # Extract prompt and create sampling parameters
         prompt = self._extract_prompt(request)
@@ -410,87 +482,110 @@ async def resolve(  # type: ignore[override]
         # Generate unique request ID for this generation
         request_id = str(uuid.uuid4())
 
-        request_info.timings.request_start = time.time()
+        if not stream:
+            # Non-streaming path
+            request_info.timings.request_start = time.time()
+            final_output: RequestOutput | None = None
 
-        # Track accumulated text for streaming
-        accumulated_text = ""
-        final_output: RequestOutput | None = None
+            try:
+                # Use AsyncLLMEngine's native async generation
+                async for request_output in self._engine.generate(  # type: ignore[misc]
+                    prompt, sampling_params, request_id
+                ):
+                    iter_time = time.time()
+                    self._update_request_timing(request_info, iter_time)
+                    final_output = request_output
 
-        try:
+                    # Check if generation is finished
+                    if (
+                        request_output.outputs
+                        and len(request_output.outputs) > 0
+                        and request_output.outputs[0].finish_reason is not None
+                    ):
+                        break
 
-            # Use AsyncLLMEngine's native async generation
-            async for request_output in self._engine.generate(  # type: ignore[misc]
-                prompt, sampling_params, request_id
-            ):
-                iter_time = time.time()
-
-                # Update request iteration timing
-                if request_info.timings.first_request_iteration is None:
-                    request_info.timings.first_request_iteration = iter_time
-                request_info.timings.last_request_iteration = iter_time
-                request_info.timings.request_iterations += 1
-
-                # Extract generated text from output
-                generated_text = ""
-                if request_output.outputs and len(request_output.outputs) > 0:
-                    generated_text = request_output.outputs[0].text
-
-                # For streaming, yield incremental updates
-                if stream:
-                    # Check if this is a new token (text has changed)
-                    if generated_text != accumulated_text:
-                        # Update token iteration timing on first new token
-                        if request_info.timings.first_token_iteration is None:
-                            request_info.timings.first_token_iteration = iter_time
-                            request_info.timings.token_iterations = 0
+                request_info.timings.request_end = time.time()
+
+                # Convert to OpenAI format and use handler
+                if final_output is not None:
+                    openai_format = self._convert_vllm_output_to_openai_format(
+                        request, final_output
+                    )
+                    yield response_handler.compile_non_streaming(request, openai_format), request_info
 
-                        request_info.timings.last_token_iteration = iter_time
-                        request_info.timings.token_iterations += 1
+            except asyncio.CancelledError as err:
+                # Yield current result to store iterative results before propagating
+                if final_output is not None:
+                    openai_format = self._convert_vllm_output_to_openai_format(
+                        request, final_output
+                    )
+                    yield response_handler.compile_non_streaming(request, openai_format), request_info
+                raise err
+            except Exception as exc:
+                # Re-raise with context
+                raise RuntimeError(f"Generation failed: {exc}") from exc
+
+        else:
+            # Streaming path
+            try:
+                request_info.timings.request_start = time.time()
+                accumulated_text = ""
+                final_output: RequestOutput | None = None
+
+                # Use AsyncLLMEngine's native async generation
+                async for request_output in self._engine.generate(  # type: ignore[misc]
+                    prompt, sampling_params, request_id
+                ):
+                    iter_time = time.time()
+                    self._update_request_timing(request_info, iter_time)
+
+                    # Extract generated text from output
+                    generated_text = ""
+                    if request_output.outputs and len(request_output.outputs) > 0:
+                        generated_text = request_output.outputs[0].text
 
-                        # Create partial response with incremental text
-                        partial_response = self._convert_output_to_response(
-                            request, request_output, accumulated_text
-                        )
-                        yield partial_response, request_info
+                    # Check if this is a new token (text has changed)
+                    if generated_text != accumulated_text:
+                        # Calculate delta (new text)
+                        delta_text = generated_text[len(accumulated_text) :]
+
+                        # Build streaming delta format based on request type
+                        if request.request_type == "text_completions":
+                            delta_data = {"choices": [{"text": delta_text}]}
+                        else:  # chat_completions
+                            delta_data = {
+                                "choices": [{"delta": {"content": delta_text, "role": "assistant"}}]
+                            }
+
+                        # Add response ID if available
+                        if hasattr(request_output, "request_id") and request_output.request_id:
+                            delta_data["id"] = request_output.request_id
+
+                        # Format as SSE line and feed to handler
+                        # json.dumps may return bytes (orjson) or str, ensure it's a string
+                        json_str = json.dumps(delta_data)
+                        if isinstance(json_str, bytes):
+                            json_str = json_str.decode("utf-8")
+                        sse_line = f"data: {json_str}"
+                        iterations = response_handler.add_streaming_line(sse_line)
+
+                        if iterations is not None and iterations > 0:
+                            self._update_token_timing(request_info, iter_time, iterations)
 
                         accumulated_text = generated_text
 
-                # Keep track of final output for non-streaming or final response
-                final_output = request_output
+                    final_output = request_output
 
-                # For non-streaming, wait for generation to finish
-                if not stream:
-                    # Check if generation is finished
-                    if (request_output.outputs and len(request_output.outputs) > 0 and
-                        request_output.outputs[0].finish_reason is not None):
-                        break
+                request_info.timings.request_end = time.time()
 
-            request_info.timings.request_end = time.time()
+                # Yield final compiled response
+                yield response_handler.compile_streaming(request), request_info
 
-            # For streaming, yield final complete response with full metrics
-            # For non-streaming, yield the complete response
-            if final_output is not None:
-                if stream:
-                    # Final response with complete metrics
-                    final_response = self._convert_output_to_response(
-                        request, final_output, ""
-                    )
-                else:
-                    # Single complete response for non-streaming
-                    final_response = self._convert_output_to_response(
-                        request, final_output, ""
-                    )
-                yield final_response, request_info
-
-        except asyncio.CancelledError as err:
-            # Yield current result to store iterative results before propagating
-            if final_output is not None:
-                response = self._convert_output_to_response(request, final_output, accumulated_text)
-                yield response, request_info
-            raise err
-        except Exception as exc:
-            # Re-raise with context
-            raise RuntimeError(
-                f"Generation failed: {exc}"
-            ) from exc
+            except asyncio.CancelledError as err:
+                # Yield current result to store iterative results before propagating
+                yield response_handler.compile_streaming(request), request_info
+                raise err
+            except Exception as exc:
+                # Re-raise with context
+                raise RuntimeError(f"Generation failed: {exc}") from exc
 

From 6f818a0eb2b937bf48a83e15ef940a12f175c94b Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Fri, 12 Dec 2025 19:19:29 -0500
Subject: [PATCH 06/56] Add way of specifying if target should be specified

Generated-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/__main__.py                      | 23 +++++++
 src/guidellm/backends/backend.py              |  9 +++
 src/guidellm/backends/openai.py               |  9 +++
 src/guidellm/backends/vllm.py                 | 11 ++++
 src/guidellm/benchmark/entrypoints.py         | 15 ++++-
 .../schemas/generative/entrypoints.py         | 61 ++++++++++++++++++-
 6 files changed, 123 insertions(+), 5 deletions(-)

diff --git a/src/guidellm/__main__.py b/src/guidellm/__main__.py
index b5f918ae5..b468a7250 100644
--- a/src/guidellm/__main__.py
+++ b/src/guidellm/__main__.py
@@ -46,6 +46,7 @@
     get_builtin_scenarios,
     reimport_benchmarks_report,
 )
+from guidellm.benchmark.schemas.generative.entrypoints import backend_requires_target
 from guidellm.mock_server import MockServer, MockServerConfig
 from guidellm.scheduler import StrategyType
 from guidellm.schemas import GenerativeRequestType
@@ -428,6 +429,28 @@ def run(**kwargs):
             status="warning",
         )
 
+    # Early validation: check target parameter based on backend requirements
+    backend = kwargs.get("backend", BenchmarkGenerativeTextArgs.get_default("backend"))
+    target = kwargs.get("target", None)
+    requires_target = backend_requires_target(backend)
+    backend_type = backend.type_ if hasattr(backend, "type_") else backend
+
+    if requires_target and target is None:
+        raise click.BadParameter(
+            f"Backend '{backend_type}' requires a target parameter. "
+            "Please provide --target with a valid endpoint URL.",
+            ctx=click.get_current_context(),
+            param_hint="--target",
+        )
+
+    if not requires_target and target is not None:
+        raise click.BadParameter(
+            f"Backend '{backend_type}' does not support a target parameter. "
+            "Please remove --target as this backend runs locally.",
+            ctx=click.get_current_context(),
+            param_hint="--target",
+        )
+
     try:
         args = BenchmarkGenerativeTextArgs.create(
             scenario=kwargs.pop("scenario", None), **kwargs
diff --git a/src/guidellm/backends/backend.py b/src/guidellm/backends/backend.py
index 94206d310..24d39c6ee 100644
--- a/src/guidellm/backends/backend.py
+++ b/src/guidellm/backends/backend.py
@@ -101,6 +101,15 @@ def requests_limit(self) -> int | None:
         """
         return None
 
+    @classmethod
+    def requires_target(cls) -> bool:
+        """
+        Indicate whether this backend requires a target parameter.
+
+        :return: True if the backend requires a target parameter, False otherwise
+        """
+        return True  # Default to True for safety (most backends need a target)
+
     @abstractmethod
     async def default_model(self) -> str:
         """
diff --git a/src/guidellm/backends/openai.py b/src/guidellm/backends/openai.py
index 57e2d95a6..3d1cedfc8 100644
--- a/src/guidellm/backends/openai.py
+++ b/src/guidellm/backends/openai.py
@@ -48,6 +48,15 @@ class OpenAIHTTPBackend(Backend):
         await backend.process_shutdown()
     """
 
+    @classmethod
+    def requires_target(cls) -> bool:
+        """
+        OpenAI HTTP backend requires a target URL.
+
+        :return: True, as this backend requires a target endpoint URL
+        """
+        return True
+
     def __init__(
         self,
         target: str,
diff --git a/src/guidellm/backends/vllm.py b/src/guidellm/backends/vllm.py
index 90dc9f1ac..0f1ba2de6 100644
--- a/src/guidellm/backends/vllm.py
+++ b/src/guidellm/backends/vllm.py
@@ -72,6 +72,17 @@ class VLLMPythonBackend(Backend):
         await backend.process_shutdown()
     """
 
+    @classmethod
+    def requires_target(cls) -> bool:
+        """
+        VLLM Python backend does not require a target URL.
+
+        This backend runs locally and does not connect to a remote server.
+
+        :return: False, as this backend does not require a target
+        """
+        return False
+
     # Default VLLM configuration
     DEFAULT_VLLM_CONFIG: dict[str, Any] = {
         "tensor_parallel_size": 1,
diff --git a/src/guidellm/benchmark/entrypoints.py b/src/guidellm/benchmark/entrypoints.py
index 5b57b22fe..e0bb1fcd6 100644
--- a/src/guidellm/benchmark/entrypoints.py
+++ b/src/guidellm/benchmark/entrypoints.py
@@ -21,6 +21,7 @@
 
 from guidellm.backends import Backend, BackendType
 from guidellm.benchmark.benchmarker import Benchmarker
+from guidellm.benchmark.schemas.generative.entrypoints import backend_requires_target
 from guidellm.benchmark.outputs import (
     GenerativeBenchmarkerConsole,
     GenerativeBenchmarkerOutput,
@@ -78,7 +79,7 @@
 
 async def resolve_backend(
     backend: BackendType | Backend,
-    target: str,
+    target: str | None,
     model: str | None,
     console: Console | None = None,
     **backend_kwargs: dict[str, Any],
@@ -92,7 +93,7 @@ async def resolve_backend(
     benchmark execution.
 
     :param backend: Backend type identifier or pre-configured Backend instance
-    :param target: Target endpoint URL or connection string for the backend
+    :param target: Target endpoint URL or connection string for the backend, or None
     :param model: Model identifier to use with the backend, or None to use default
     :param console: Console instance for progress reporting, or None
     :param backend_kwargs: Additional keyword arguments passed to backend initialization
@@ -103,8 +104,16 @@ async def resolve_backend(
         if console
         else None
     )
+
+    # Only pass target if it's provided (some backends don't need it)
+    create_kwargs = {**(backend_kwargs or {})}
+    if target is not None:
+        create_kwargs["target"] = target
+    if model is not None:
+        create_kwargs["model"] = model
+
     backend_instance = (
-        Backend.create(backend, target=target, model=model, **(backend_kwargs or {}))
+        Backend.create(backend, **create_kwargs)
         if not isinstance(backend, Backend)
         else backend
     )
diff --git a/src/guidellm/benchmark/schemas/generative/entrypoints.py b/src/guidellm/benchmark/schemas/generative/entrypoints.py
index a080daa03..1580de802 100644
--- a/src/guidellm/benchmark/schemas/generative/entrypoints.py
+++ b/src/guidellm/benchmark/schemas/generative/entrypoints.py
@@ -27,6 +27,7 @@
     ValidatorFunctionWrapHandler,
     field_serializer,
     field_validator,
+    model_validator,
 )
 from torch.utils.data import Sampler
 from transformers import PreTrainedTokenizerBase
@@ -39,7 +40,30 @@
 from guidellm.scheduler import StrategyType
 from guidellm.schemas import StandardBaseModel
 
-__all__ = ["BenchmarkGenerativeTextArgs"]
+__all__ = ["BenchmarkGenerativeTextArgs", "backend_requires_target"]
+
+
+def backend_requires_target(backend: BackendType | Backend) -> bool:
+    """
+    Determine if a backend requires a target parameter.
+
+    Uses the backend's class method to determine if it requires target,
+    making it extensible for new backend implementations.
+
+    :param backend: Backend type identifier or Backend instance
+    :return: True if the backend requires target, False otherwise
+    """
+    if isinstance(backend, Backend):
+        # If it's already a Backend instance, use its class method
+        return backend.__class__.requires_target()
+
+    # If it's a BackendType string, get the registered backend class
+    backend_class = Backend.get_registered_object(backend)
+    if backend_class is None:
+        # Unknown backend type, default to True for safety
+        return True
+
+    return backend_class.requires_target()
 
 
 class BenchmarkGenerativeTextArgs(StandardBaseModel):
@@ -159,7 +183,9 @@ def get_default(cls: type[BenchmarkGenerativeTextArgs], field: str) -> Any:
     )
 
     # Required
-    target: str = Field(description="Target endpoint URL for benchmark execution")
+    target: str | None = Field(
+        default=None, description="Target endpoint URL for benchmark execution"
+    )
     data: list[Any] = Field(
         description="List of dataset sources or data files",
         default_factory=list,
@@ -304,6 +330,37 @@ def single_to_list(
             else:
                 raise
 
+    @model_validator(mode="after")
+    def validate_target_required(self) -> BenchmarkGenerativeTextArgs:
+        """
+        Validate target parameter based on backend requirements.
+
+        - If backend requires target: target must be provided (not None)
+        - If backend does not require target: target must be None (not provided)
+
+        :return: Self if validation passes
+        :raises ValueError: If target is provided when backend doesn't support it,
+            or if target is missing when backend requires it
+        """
+        backend_type = (
+            self.backend.type_ if isinstance(self.backend, Backend) else self.backend
+        )
+        requires_target = backend_requires_target(self.backend)
+
+        if requires_target and self.target is None:
+            raise ValueError(
+                f"Backend '{backend_type}' requires a target parameter. "
+                "Please provide --target with a valid endpoint URL."
+            )
+
+        if not requires_target and self.target is not None:
+            raise ValueError(
+                f"Backend '{backend_type}' does not support a target parameter. "
+                "Please remove --target as this backend runs locally."
+            )
+
+        return self
+
     @field_serializer("backend")
     def serialize_backend(self, backend: BackendType | Backend) -> str:
         """Serialize backend to type string."""

From ef60f158ca39ec7e31caf546d09887ce9730b60e Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 15 Dec 2025 13:04:30 -0500
Subject: [PATCH 07/56] Add code for backend model requirements

Generated-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/__main__.py                      | 19 +++++++-
 src/guidellm/backends/backend.py              |  9 ++++
 src/guidellm/backends/openai.py               | 11 +++++
 src/guidellm/backends/vllm.py                 | 11 +++++
 .../schemas/generative/entrypoints.py         | 48 +++++++++++++++++--
 5 files changed, 93 insertions(+), 5 deletions(-)

diff --git a/src/guidellm/__main__.py b/src/guidellm/__main__.py
index b468a7250..32d626cc5 100644
--- a/src/guidellm/__main__.py
+++ b/src/guidellm/__main__.py
@@ -46,7 +46,10 @@
     get_builtin_scenarios,
     reimport_benchmarks_report,
 )
-from guidellm.benchmark.schemas.generative.entrypoints import backend_requires_target
+from guidellm.benchmark.schemas.generative.entrypoints import (
+    backend_requires_model,
+    backend_requires_target,
+)
 from guidellm.mock_server import MockServer, MockServerConfig
 from guidellm.scheduler import StrategyType
 from guidellm.schemas import GenerativeRequestType
@@ -429,12 +432,15 @@ def run(**kwargs):
             status="warning",
         )
 
-    # Early validation: check target parameter based on backend requirements
+    # Early validation: check target and model parameters based on backend requirements
     backend = kwargs.get("backend", BenchmarkGenerativeTextArgs.get_default("backend"))
     target = kwargs.get("target", None)
+    model = kwargs.get("model", None)
     requires_target = backend_requires_target(backend)
+    requires_model = backend_requires_model(backend)
     backend_type = backend.type_ if hasattr(backend, "type_") else backend
 
+    # Validate target parameter
     if requires_target and target is None:
         raise click.BadParameter(
             f"Backend '{backend_type}' requires a target parameter. "
@@ -451,6 +457,15 @@ def run(**kwargs):
             param_hint="--target",
         )
 
+    # Validate model parameter
+    if requires_model and model is None:
+        raise click.BadParameter(
+            f"Backend '{backend_type}' requires a model parameter. "
+            "Please provide --model with a valid model identifier.",
+            ctx=click.get_current_context(),
+            param_hint="--model",
+        )
+
     try:
         args = BenchmarkGenerativeTextArgs.create(
             scenario=kwargs.pop("scenario", None), **kwargs
diff --git a/src/guidellm/backends/backend.py b/src/guidellm/backends/backend.py
index 24d39c6ee..998f3bf78 100644
--- a/src/guidellm/backends/backend.py
+++ b/src/guidellm/backends/backend.py
@@ -110,6 +110,15 @@ def requires_target(cls) -> bool:
         """
         return True  # Default to True for safety (most backends need a target)
 
+    @classmethod
+    def requires_model(cls) -> bool:
+        """
+        Indicate whether this backend requires a model parameter.
+
+        :return: True if the backend requires a model parameter, False otherwise
+        """
+        return False  # Default to False (model is optional by default)
+
     @abstractmethod
     async def default_model(self) -> str:
         """
diff --git a/src/guidellm/backends/openai.py b/src/guidellm/backends/openai.py
index 3d1cedfc8..67469fd0f 100644
--- a/src/guidellm/backends/openai.py
+++ b/src/guidellm/backends/openai.py
@@ -57,6 +57,17 @@ def requires_target(cls) -> bool:
         """
         return True
 
+    @classmethod
+    def requires_model(cls) -> bool:
+        """
+        OpenAI HTTP backend does not require a model parameter.
+
+        The model can be optional as the backend can use the default model from the server.
+
+        :return: False, as this backend does not require a model parameter
+        """
+        return False
+
     def __init__(
         self,
         target: str,
diff --git a/src/guidellm/backends/vllm.py b/src/guidellm/backends/vllm.py
index 0f1ba2de6..8474753c7 100644
--- a/src/guidellm/backends/vllm.py
+++ b/src/guidellm/backends/vllm.py
@@ -83,6 +83,17 @@ def requires_target(cls) -> bool:
         """
         return False
 
+    @classmethod
+    def requires_model(cls) -> bool:
+        """
+        VLLM Python backend requires a model parameter.
+
+        The model must be specified to know which model to load for inference.
+
+        :return: True, as this backend requires a model parameter
+        """
+        return True
+
     # Default VLLM configuration
     DEFAULT_VLLM_CONFIG: dict[str, Any] = {
         "tensor_parallel_size": 1,
diff --git a/src/guidellm/benchmark/schemas/generative/entrypoints.py b/src/guidellm/benchmark/schemas/generative/entrypoints.py
index 1580de802..138d2e297 100644
--- a/src/guidellm/benchmark/schemas/generative/entrypoints.py
+++ b/src/guidellm/benchmark/schemas/generative/entrypoints.py
@@ -40,7 +40,11 @@
 from guidellm.scheduler import StrategyType
 from guidellm.schemas import StandardBaseModel
 
-__all__ = ["BenchmarkGenerativeTextArgs", "backend_requires_target"]
+__all__ = [
+    "BenchmarkGenerativeTextArgs",
+    "backend_requires_target",
+    "backend_requires_model",
+]
 
 
 def backend_requires_target(backend: BackendType | Backend) -> bool:
@@ -66,6 +70,29 @@ def backend_requires_target(backend: BackendType | Backend) -> bool:
     return backend_class.requires_target()
 
 
+def backend_requires_model(backend: BackendType | Backend) -> bool:
+    """
+    Determine if a backend requires a model parameter.
+
+    Uses the backend's class method to determine if it requires model,
+    making it extensible for new backend implementations.
+
+    :param backend: Backend type identifier or Backend instance
+    :return: True if the backend requires model, False otherwise
+    """
+    if isinstance(backend, Backend):
+        # If it's already a Backend instance, use its class method
+        return backend.__class__.requires_model()
+
+    # If it's a BackendType string, get the registered backend class
+    backend_class = Backend.get_registered_object(backend)
+    if backend_class is None:
+        # Unknown backend type, default to False for safety
+        return False
+
+    return backend_class.requires_model()
+
+
 class BenchmarkGenerativeTextArgs(StandardBaseModel):
     """
     Configuration arguments for generative text benchmark execution.
@@ -333,20 +360,28 @@ def single_to_list(
     @model_validator(mode="after")
     def validate_target_required(self) -> BenchmarkGenerativeTextArgs:
         """
-        Validate target parameter based on backend requirements.
+        Validate target and model parameters based on backend requirements.
 
+        Target validation:
         - If backend requires target: target must be provided (not None)
         - If backend does not require target: target must be None (not provided)
 
+        Model validation:
+        - If backend requires model: model must be provided (not None)
+        - If backend does not require model: model can be None or provided
+
         :return: Self if validation passes
         :raises ValueError: If target is provided when backend doesn't support it,
-            or if target is missing when backend requires it
+            if target is missing when backend requires it,
+            or if model is missing when backend requires it
         """
         backend_type = (
             self.backend.type_ if isinstance(self.backend, Backend) else self.backend
         )
         requires_target = backend_requires_target(self.backend)
+        requires_model = backend_requires_model(self.backend)
 
+        # Validate target parameter
         if requires_target and self.target is None:
             raise ValueError(
                 f"Backend '{backend_type}' requires a target parameter. "
@@ -359,6 +394,13 @@ def validate_target_required(self) -> BenchmarkGenerativeTextArgs:
                 "Please remove --target as this backend runs locally."
             )
 
+        # Validate model parameter
+        if requires_model and self.model is None:
+            raise ValueError(
+                f"Backend '{backend_type}' requires a model parameter. "
+                "Please provide --model with a valid model identifier."
+            )
+
         return self
 
     @field_serializer("backend")

From 6707b1ddbf7c86d7360b51381a75447239dba948 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 15 Dec 2025 17:26:50 -0500
Subject: [PATCH 08/56] Initial support for audio requests

Generated-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm.py | 174 +++++++++++++++++++++++++++++++---
 1 file changed, 161 insertions(+), 13 deletions(-)

diff --git a/src/guidellm/backends/vllm.py b/src/guidellm/backends/vllm.py
index 8474753c7..ec8fccc87 100644
--- a/src/guidellm/backends/vllm.py
+++ b/src/guidellm/backends/vllm.py
@@ -15,6 +15,8 @@
 from collections.abc import AsyncIterator
 from typing import Any
 
+import numpy as np
+
 try:
     from vllm import SamplingParams
     from vllm.engine.async_llm_engine import AsyncLLMEngine
@@ -31,6 +33,7 @@
 
 from guidellm.backends.backend import Backend
 from guidellm.backends.response_handlers import GenerationResponseHandlerFactory
+from guidellm.extras.audio import _decode_audio
 from guidellm.schemas import GenerationRequest, GenerationResponse, RequestInfo, UsageMetrics
 from guidellm.utils import json
 
@@ -270,10 +273,16 @@ def _validate_request_type(self, request_type: str) -> None:
         :param request_type: The request type to validate
         :raises ValueError: If request type is not supported
         """
-        if request_type not in ("text_completions", "chat_completions"):
+        supported_types = (
+            "text_completions",
+            "chat_completions",
+            "audio_transcriptions",
+            "audio_translations",
+        )
+        if request_type not in supported_types:
             raise ValueError(
                 f"Unsupported request type '{request_type}'. "
-                "Only 'text_completions' and 'chat_completions' are supported."
+                f"Supported types are: {', '.join(supported_types)}."
             )
 
     def _update_request_timing(self, request_info: RequestInfo, iter_time: float) -> None:
@@ -332,6 +341,58 @@ def _extract_text_from_content(self, content: str | list[dict[str, Any]] | Any)
         # Fallback: convert to string
         return str(content) if content is not None else ""
 
+    def _extract_audio_from_request(self, request: GenerationRequest) -> tuple[bytes, str] | None:
+        """
+        Extract audio file from generation request.
+
+        Extracts audio file data and mimetype from request.arguments.files.
+        Audio files are expected to be in the format: (filename, bytes, mimetype).
+
+        :param request: Generation request with files containing audio data
+        :return: Tuple of (audio_bytes, mimetype) or None if no audio file found
+        :raises ValueError: If audio file is missing or invalid for audio request types
+        """
+        if request.request_type not in ("audio_transcriptions", "audio_translations"):
+            return None
+
+        files = request.arguments.files or {}
+        if not files:
+            raise ValueError(
+                f"{request.request_type} request must include audio file in 'files'"
+            )
+
+        # Find the audio file (typically keyed as "file")
+        audio_file = None
+        for key, value in files.items():
+            if isinstance(value, (list, tuple)) and len(value) >= 2:
+                # Format: (filename, bytes, mimetype) or [filename, bytes, mimetype]
+                audio_file = value
+                break
+            elif isinstance(value, bytes):
+                # Direct bytes, use default filename and mimetype
+                audio_file = ("audio.wav", value, "audio/wav")
+                break
+
+        if audio_file is None:
+            raise ValueError(
+                f"{request.request_type} request must include audio file in 'files'"
+            )
+
+        # Extract bytes and mimetype
+        if isinstance(audio_file, (list, tuple)):
+            if len(audio_file) >= 2:
+                # Ensure we have actual bytes, not an integer or other type
+                audio_data = audio_file[1]
+                if not isinstance(audio_data, bytes):
+                    raise ValueError(
+                        f"Expected bytes for audio data, got {type(audio_data)}: {audio_data}"
+                    )
+                audio_bytes = audio_data
+                mimetype = audio_file[2] if len(audio_file) >= 3 else "audio/wav"
+                return (audio_bytes, mimetype)
+
+        raise ValueError(f"Invalid audio file format in {request.request_type} request")
+
     def _extract_prompt(self, request: GenerationRequest) -> str:
         """
         Extract prompt text from generation request.
@@ -387,6 +448,12 @@ def _extract_prompt(self, request: GenerationRequest) -> str:
             else:
                 raise RuntimeError("Code not setup for state")
 
+        if request.request_type in ("audio_transcriptions", "audio_translations"):
+            # For audio requests, prompt is optional (e.g., language hints)
+            body = request.arguments.body or {}
+            prompt = body.get("prompt", "")
+            return prompt if isinstance(prompt, str) else str(prompt) if prompt else ""
+
         raise ValueError(f"Unsupported request type: {request.request_type}")
 
     def _create_sampling_params(self, request: GenerationRequest) -> SamplingParams:
@@ -446,19 +513,26 @@ def _convert_vllm_output_to_openai_format(
             "total_tokens": input_tokens + output_tokens,
         }
 
-        # Build choices array based on request type
-        if request.request_type == "text_completions":
-            choices = [{"text": generated_text}]
-        elif request.request_type == "chat_completions":
-            choices = [{"message": {"content": generated_text, "role": "assistant"}}]
+        # Audio responses use different format: {"text": "...", "usage": {...}}
+        if request.request_type in ("audio_transcriptions", "audio_translations"):
+            response_dict: dict[str, Any] = {
+                "text": generated_text,
+                "usage": usage,
+            }
         else:
-            choices = [{"text": generated_text}]
+            # Build choices array based on request type
+            if request.request_type == "text_completions":
+                choices = [{"text": generated_text}]
+            elif request.request_type == "chat_completions":
+                choices = [{"message": {"content": generated_text, "role": "assistant"}}]
+            else:
+                choices = [{"text": generated_text}]
 
-        # Build response dict
-        response_dict: dict[str, Any] = {
-            "choices": choices,
-            "usage": usage,
-        }
+            # Build response dict
+            response_dict = {
+                "choices": choices,
+                "usage": usage,
+            }
 
         # Add response ID if available
         if hasattr(output, "request_id") and output.request_id:
@@ -501,6 +575,61 @@ async def resolve(  # type: ignore[override]
         sampling_params = self._create_sampling_params(request)
         stream = request.arguments.stream or False
 
+        # For audio requests, construct multimodal input with audio data
+        if request.request_type in ("audio_transcriptions", "audio_translations"):
+            audio_data = self._extract_audio_from_request(request)
+            if audio_data is None:
+                raise ValueError(
+                    f"{request.request_type} request must include audio file in 'files'"
+                )
+            audio_bytes, mimetype = audio_data
+
+            # Validate audio_bytes is actually bytes
+            if not isinstance(audio_bytes, bytes):
+                raise TypeError(
+                    f"Expected bytes for audio data, got {type(audio_bytes)}: {audio_bytes}"
+                )
+            if len(audio_bytes) == 0:
+                raise ValueError("Audio data cannot be empty")
+
+            # Decode audio bytes to numpy array for vLLM
+            # vLLM's _get_audio_with_sr expects decoded audio samples (numpy array, torch tensor, etc.)
+            # not raw encoded bytes
+            try:
+                audio_samples = _decode_audio(audio_bytes)
+                # Convert torch tensor to numpy array (vLLM accepts numpy arrays)
+                if hasattr(audio_samples.data, 'numpy'):
+                    audio_array = audio_samples.data.numpy()
+                elif hasattr(audio_samples.data, 'cpu'):
+                    audio_array = audio_samples.data.cpu().numpy()
+                else:
+                    # Already a numpy array or compatible
+                    audio_array = np.asarray(audio_samples.data)
+                
+                # vLLM can accept either:
+                # 1. Just the numpy array (orig_sr will be None, no resampling)
+                # 2. Tuple (array, sample_rate) - but this triggers resampling which needs target_sr
+                # Since the resampler may not have target_sr configured, pass just the array
+                # The model should handle the sample rate appropriately
+                audio_for_vllm = audio_array
+            except Exception as exc:
+                raise ValueError(
+                    f"Failed to decode audio data for vLLM: {exc}"
+                ) from exc
+
+            # Construct multimodal prompt for vLLM v1 transcription models
+            # vLLM v1's generate method accepts multimodal data in this format
+            # The prompt should be a string, and multimodal data passed as a dict
+            # Note: vLLM's _parse_audio_data automatically wraps single arrays in a list
+            prompt_text = prompt if isinstance(prompt, str) else ""
+            
+            prompt = {
+                "prompt": prompt_text,
+                "multi_modal_data": {
+                    "audio": audio_for_vllm,
+                },
+            }
+
         # Generate unique request ID for this generation
         request_id = str(uuid.uuid4())
 
@@ -544,6 +673,14 @@ async def resolve(  # type: ignore[override]
                     yield response_handler.compile_non_streaming(request, openai_format), request_info
                 raise err
             except Exception as exc:
+                # Provide clearer error message for audio-related issues
+                error_msg = str(exc)
+                if "At most 0 audio" in error_msg or "audio(s) may be provided" in error_msg:
+                    raise RuntimeError(
+                        f"Generation failed: The model '{self.model}' does not support audio inputs. "
+                        f"For audio transcriptions, please use an audio-capable model (e.g., Whisper-based models). "
+                        f"Original error: {exc}"
+                    ) from exc
                 # Re-raise with context
                 raise RuntimeError(f"Generation failed: {exc}") from exc
 
@@ -574,6 +711,9 @@ async def resolve(  # type: ignore[override]
                         # Build streaming delta format based on request type
                         if request.request_type == "text_completions":
                             delta_data = {"choices": [{"text": delta_text}]}
+                        elif request.request_type in ("audio_transcriptions", "audio_translations"):
+                            # Audio streaming uses {"text": "..."} format
+                            delta_data = {"text": delta_text}
                         else:  # chat_completions
                             delta_data = {
                                 "choices": [{"delta": {"content": delta_text, "role": "assistant"}}]
@@ -608,6 +748,14 @@ async def resolve(  # type: ignore[override]
                 yield response_handler.compile_streaming(request), request_info
                 raise err
             except Exception as exc:
+                # Provide clearer error message for audio-related issues
+                error_msg = str(exc)
+                if "At most 0 audio" in error_msg or "audio(s) may be provided" in error_msg:
+                    raise RuntimeError(
+                        f"Generation failed: The model '{self.model}' does not support audio inputs. "
+                        f"For audio transcriptions, please use an audio-capable model (e.g., Whisper-based models). "
+                        f"Original error: {exc}"
+                    ) from exc
                 # Re-raise with context
                 raise RuntimeError(f"Generation failed: {exc}") from exc
 

From afa992a33545f5d233e46788e42c4b30d9c90d7a Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 17 Dec 2025 13:03:47 -0500
Subject: [PATCH 09/56] Start switching over to VLLM's image as base image

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/Containerfile b/Containerfile
index 206473086..1f42baea5 100644
--- a/Containerfile
+++ b/Containerfile
@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=quay.io/fedora/python-313-minimal:latest
+ARG BASE_IMAGE=vllm/vllm-openai
 
 # Use a multi-stage build to create a lightweight production image
 FROM $BASE_IMAGE as builder
@@ -13,11 +13,15 @@ ARG GUIDELLM_BUILD_TYPE=dev
 # Switch to root for installing packages
 USER root
 
-# Install build tooling
-RUN dnf install -y git \
+RUN apt-get update || true \
+    && apt-get install -y --no-install-recommends gnupg ca-certificates \
+    && apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 871920D1991BC93C \
+    && apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys BA6932366A755776 \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN apt-get update && apt-get install -y git \
     && /usr/bin/python3 -m venv /tmp/uv \
-    && /tmp/uv/bin/pip install --no-cache-dir -U uv \
-    && ln -s /tmp/uv/bin/uv /usr/local/bin/uv
+    && /tmp/uv/bin/pip install --no-cache-dir -U uv
 
 # Set the default venv for uv
 # Copy instead of link files with uv
@@ -40,9 +44,9 @@ FROM $BASE_IMAGE
 USER root
 
 # Install some helpful utilities and deps
-RUN dnf install -y --setopt=install_weak_deps=False \
+RUN apt-get install -y --no-install-recommends \
         vi tar rsync ffmpeg-free \
-    && dnf clean all
+    && apt clean
 
 # Switch back to unpriv user
 # Root group for k8s

From f82bf3bc2d482e3657e4ef3ee8faad8076801b77 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 17 Dec 2025 16:26:23 -0500
Subject: [PATCH 10/56] Use base image's components

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/Containerfile b/Containerfile
index 1f42baea5..acb17f217 100644
--- a/Containerfile
+++ b/Containerfile
@@ -20,8 +20,7 @@ RUN apt-get update || true \
     && rm -rf /var/lib/apt/lists/*
 
 RUN apt-get update && apt-get install -y git \
-    && /usr/bin/python3 -m venv /tmp/uv \
-    && /tmp/uv/bin/pip install --no-cache-dir -U uv
+    && rm -rf /var/lib/apt/lists/*
 
 # Set the default venv for uv
 # Copy instead of link files with uv
@@ -35,6 +34,9 @@ ENV VIRTUAL_ENV=/opt/app-root \
 COPY / /src
 
 # Install guidellm and locked dependencies
+# Have it use system packages (where vllm is installed)
+RUN uv venv /opt/app-root --system-site-packages
+
 RUN uv sync --active --project /src --frozen --no-dev --extra all --no-editable
 
 # Prod image
@@ -44,9 +46,8 @@ FROM $BASE_IMAGE
 USER root
 
 # Install some helpful utilities and deps
-RUN apt-get install -y --no-install-recommends \
-        vi tar rsync ffmpeg-free \
-    && apt clean
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        vim tar rsync ffmpeg \
 
 # Switch back to unpriv user
 # Root group for k8s

From 5a945c3126acf68262de5d29ae1ddb524e46e503 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 17 Dec 2025 16:30:05 -0500
Subject: [PATCH 11/56] Fix missing line in Containerfile

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Containerfile b/Containerfile
index acb17f217..976839b51 100644
--- a/Containerfile
+++ b/Containerfile
@@ -48,6 +48,7 @@ USER root
 # Install some helpful utilities and deps
 RUN apt-get update && apt-get install -y --no-install-recommends \
         vim tar rsync ffmpeg \
+        && apt clean
 
 # Switch back to unpriv user
 # Root group for k8s

From 218fbb567b0d60fc24efa233e7bd66e6803e235e Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 17 Dec 2025 18:38:29 -0500
Subject: [PATCH 12/56] Remove conflicting packages

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/Containerfile b/Containerfile
index 976839b51..9d68806b8 100644
--- a/Containerfile
+++ b/Containerfile
@@ -37,7 +37,11 @@ COPY / /src
 # Have it use system packages (where vllm is installed)
 RUN uv venv /opt/app-root --system-site-packages
 
-RUN uv sync --active --project /src --frozen --no-dev --extra all --no-editable
+# Install dependencies, then remove packages that conflict with base image
+# The base image already has compatible versions of torch, torchvision, transformers, and vllm
+# Removing them from venv ensures Python uses the base image versions via system-site-packages
+RUN uv sync --active --project /src --frozen --no-dev --extra all --no-editable && \
+    /opt/app-root/bin/pip uninstall -y torch torchvision transformers vllm || true
 
 # Prod image
 FROM $BASE_IMAGE

From 69aa524528d16dd2fe33adbd5eb17b6ee6477050 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 17 Dec 2025 20:10:13 -0500
Subject: [PATCH 13/56] Try fix for container error

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile  |  9 +++------
 pyproject.toml | 10 +++++-----
 2 files changed, 8 insertions(+), 11 deletions(-)

diff --git a/Containerfile b/Containerfile
index 9d68806b8..628537085 100644
--- a/Containerfile
+++ b/Containerfile
@@ -34,14 +34,11 @@ ENV VIRTUAL_ENV=/opt/app-root \
 COPY / /src
 
 # Install guidellm and locked dependencies
-# Have it use system packages (where vllm is installed)
+# Have it use system packages (where vllm, torch, and transformers are installed)
+# Use the 'container' extra which excludes pytorch and vllm since they're in the base image
 RUN uv venv /opt/app-root --system-site-packages
 
-# Install dependencies, then remove packages that conflict with base image
-# The base image already has compatible versions of torch, torchvision, transformers, and vllm
-# Removing them from venv ensures Python uses the base image versions via system-site-packages
-RUN uv sync --active --project /src --frozen --no-dev --extra all --no-editable && \
-    /opt/app-root/bin/pip uninstall -y torch torchvision transformers vllm || true
+RUN uv sync --active --project /src --frozen --no-dev --extra container --no-editable
 
 # Prod image
 FROM $BASE_IMAGE
diff --git a/pyproject.toml b/pyproject.toml
index dc647e8cb..815cd7840 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -67,24 +67,24 @@ dependencies = [
     "rich",
     "sanic",
     "tabulate",
-    "transformers",
     "uvloop>=0.18",
-    "torch",
 ]
 
 [project.optional-dependencies]
 # Meta Extras
-all = ["guidellm[perf,openai,audio,vision,vllm]"]
-recommended = ["guidellm[perf,openai]"]
+all = ["guidellm[perf,openai,audio,vision,vllm,pytorch]"]
+recommended = ["guidellm[perf,openai,pytorch]"]
+container = ["guidellm[perf,openai,audio,vision]"]
 # Feature Extras
 perf = ["orjson", "msgpack", "msgspec", "uvloop"]
 openai = ["tiktoken>=0.11.0", "blobfile>=3.1.0"]
+pytorch = ["torch", "transformers"]
 vllm = ["vllm>=0.6.0"]
 audio = [
     # Lowest version with full torchcodec support
     "datasets[audio]>=4.1.0",
     # Torchcodec needs specific torch version
-    "torch==2.9.*",
+    # Note: torch is provided by pytorch extra or base image in containers
     "torchcodec==0.8.*",
 ]
 vision = [

From 48048e5b6a3201e425efb6ad363ef36ee445ecfb Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 17 Dec 2025 20:12:06 -0500
Subject: [PATCH 14/56] Try fix for container error

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Containerfile b/Containerfile
index 628537085..c8bd4604e 100644
--- a/Containerfile
+++ b/Containerfile
@@ -35,10 +35,10 @@ COPY / /src
 
 # Install guidellm and locked dependencies
 # Have it use system packages (where vllm, torch, and transformers are installed)
-# Use the 'container' extra which excludes pytorch and vllm since they're in the base image
+# Use individual extras (perf,openai,audio,vision) which exclude pytorch and vllm since they're in the base image
 RUN uv venv /opt/app-root --system-site-packages
 
-RUN uv sync --active --project /src --frozen --no-dev --extra container --no-editable
+RUN uv sync --active --project /src --frozen --no-dev --extra perf,openai,audio,vision --no-editable
 
 # Prod image
 FROM $BASE_IMAGE

From 6275e147d7ad22c8df78dd70adb794d2405a4ced Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 17 Dec 2025 20:12:56 -0500
Subject: [PATCH 15/56] Try fix for container error

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Containerfile b/Containerfile
index c8bd4604e..3348bf18c 100644
--- a/Containerfile
+++ b/Containerfile
@@ -38,7 +38,7 @@ COPY / /src
 # Use individual extras (perf,openai,audio,vision) which exclude pytorch and vllm since they're in the base image
 RUN uv venv /opt/app-root --system-site-packages
 
-RUN uv sync --active --project /src --frozen --no-dev --extra perf,openai,audio,vision --no-editable
+RUN uv sync --active --project /src --frozen --no-dev --extra perf --extra openai --extra audio --extra vision --no-editable
 
 # Prod image
 FROM $BASE_IMAGE

From 0c393c136f9b458089041009264476e99b270016 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Fri, 19 Dec 2025 17:31:37 -0500
Subject: [PATCH 16/56] Attempt to fix issues with copying venv

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile | 24 ++++++++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/Containerfile b/Containerfile
index 3348bf18c..d63744e49 100644
--- a/Containerfile
+++ b/Containerfile
@@ -51,10 +51,6 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         vim tar rsync ffmpeg \
         && apt clean
 
-# Switch back to unpriv user
-# Root group for k8s
-USER 1001:0
-
 # Add guidellm bin to PATH
 # Argument defaults can be set with GUIDELLM_<ARG>
 ENV HOME="/home/guidellm" \
@@ -77,5 +73,25 @@ LABEL io.k8s.display-name="GuideLLM" \
 # Do this as late as possible to leverage layer caching
 COPY --chown=1001:0 --from=builder /opt/app-root /opt/app-root
 
+# Fix venv paths after copying to ensure system-site-packages works correctly
+# The venv may have hardcoded paths from the builder stage that need to be updated
+# Run as root to modify files, then fix ownership
+RUN python3 -c "import sys; print(sys.executable)" > /tmp/python_path.txt && \
+    PYTHON_PATH=$(cat /tmp/python_path.txt) && \
+    PYTHON_DIR=$(dirname "$PYTHON_PATH") && \
+    # Update pyvenv.cfg with correct Python path and ensure system-site-packages is enabled
+    sed -i "s|^home = .*|home = $PYTHON_DIR|" /opt/app-root/pyvenv.cfg 2>/dev/null || true && \
+    grep -q "include-system-site-packages = true" /opt/app-root/pyvenv.cfg || \
+    echo "include-system-site-packages = true" >> /opt/app-root/pyvenv.cfg && \
+    # Fix shebang paths in Python executables
+    find /opt/app-root/bin -type f -executable -exec sed -i "1s|^#!.*python.*|#!$PYTHON_PATH|" {} \; 2>/dev/null || true && \
+    rm -f /tmp/python_path.txt && \
+    # Fix ownership back to 1001:0
+    chown -R 1001:0 /opt/app-root
+
+# Switch back to unpriv user
+# Root group for k8s
+USER 1001:0
+
 ENTRYPOINT [ "/opt/app-root/bin/guidellm" ]
 CMD [ "benchmark", "run" ]

From 40552d74786dbb177c1fa4de718b615a4afbc6a2 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 22 Dec 2025 15:47:03 -0500
Subject: [PATCH 17/56] Use separate Containerfile for vllm

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile      | 81 ++++++++++++++++++++---------------------
 Containerfile.vllm | 90 ++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 129 insertions(+), 42 deletions(-)
 create mode 100644 Containerfile.vllm

diff --git a/Containerfile b/Containerfile
index d63744e49..558c2ed9f 100644
--- a/Containerfile
+++ b/Containerfile
@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai
+ARG BASE_IMAGE=quay.io/fedora/python-313-minimal:latest
 
 # Use a multi-stage build to create a lightweight production image
 FROM $BASE_IMAGE as builder
@@ -10,35 +10,48 @@ FROM $BASE_IMAGE as builder
 # dev: increment to next minor, add 'dev' with build iteration
 ARG GUIDELLM_BUILD_TYPE=dev
 
+# Extra dependencies to install
+# all: install all extras
+# recommended: install recommended extras
+# container: All extras except vllm
+ARG GUIDELLM_BUILD_EXTRAS=container
+
 # Switch to root for installing packages
 USER root
 
-RUN apt-get update || true \
-    && apt-get install -y --no-install-recommends gnupg ca-certificates \
-    && apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 871920D1991BC93C \
-    && apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys BA6932366A755776 \
-    && rm -rf /var/lib/apt/lists/*
+# Install uv in a temporary venv
+RUN /usr/bin/python3 -m venv /tmp/uv \
+    && /tmp/uv/bin/pip install --no-cache-dir -U uv \
+    && ln -s /tmp/uv/bin/uv /usr/local/bin/uv
 
-RUN apt-get update && apt-get install -y git \
-    && rm -rf /var/lib/apt/lists/*
+# Install build dependencies
+RUN --mount=type=cache,sharing=locked,target=/var/cache/dnf \
+    dnf install -y git
 
-# Set the default venv for uv
-# Copy instead of link files with uv
 # Set correct build type for versioning
-ENV VIRTUAL_ENV=/opt/app-root \
+# Configure uv for building guidellm
+ENV GUIDELLM_BUILD_TYPE=$GUIDELLM_BUILD_TYPE \
+    VIRTUAL_ENV=/opt/app-root \
+    UV_PROJECT="/src" \
     UV_LINK_MODE="copy" \
-    GUIDELLM_BUILD_TYPE=$GUIDELLM_BUILD_TYPE
+    UV_NO_DEV="1" \
+    UV_NO_EDITABLE="1" \
+    UV_FROZEN="1" \
+    UV_CACHE_DIR="/tmp/uv_cache"
+
+# Sync initial environment
+RUN --mount=type=cache,target=$UV_CACHE_DIR \
+    --mount=type=bind,source=uv.lock,target=$UV_PROJECT/uv.lock,relabel=shared \
+    --mount=type=bind,source=pyproject.toml,target=$UV_PROJECT/pyproject.toml,relabel=shared \
+    uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS
 
 # Copy repository files
 # Do this as late as possible to leverage layer caching
-COPY / /src
-
-# Install guidellm and locked dependencies
-# Have it use system packages (where vllm, torch, and transformers are installed)
-# Use individual extras (perf,openai,audio,vision) which exclude pytorch and vllm since they're in the base image
-RUN uv venv /opt/app-root --system-site-packages
+COPY / $UV_PROJECT
 
-RUN uv sync --active --project /src --frozen --no-dev --extra perf --extra openai --extra audio --extra vision --no-editable
+# Install guidellm
+RUN --mount=type=cache,target=$UV_CACHE_DIR \
+    uv sync --active --extra $GUIDELLM_BUILD_EXTRAS
 
 # Prod image
 FROM $BASE_IMAGE
@@ -47,9 +60,13 @@ FROM $BASE_IMAGE
 USER root
 
 # Install some helpful utilities and deps
-RUN apt-get update && apt-get install -y --no-install-recommends \
-        vim tar rsync ffmpeg \
-        && apt clean
+RUN --mount=type=cache,sharing=locked,target=/var/cache/dnf \
+    dnf install -y --setopt=install_weak_deps=False \
+        vi tar rsync ffmpeg-free
+
+# Switch back to unpriv user
+# Root group for k8s
+USER 1001:0
 
 # Add guidellm bin to PATH
 # Argument defaults can be set with GUIDELLM_<ARG>
@@ -73,25 +90,5 @@ LABEL io.k8s.display-name="GuideLLM" \
 # Do this as late as possible to leverage layer caching
 COPY --chown=1001:0 --from=builder /opt/app-root /opt/app-root
 
-# Fix venv paths after copying to ensure system-site-packages works correctly
-# The venv may have hardcoded paths from the builder stage that need to be updated
-# Run as root to modify files, then fix ownership
-RUN python3 -c "import sys; print(sys.executable)" > /tmp/python_path.txt && \
-    PYTHON_PATH=$(cat /tmp/python_path.txt) && \
-    PYTHON_DIR=$(dirname "$PYTHON_PATH") && \
-    # Update pyvenv.cfg with correct Python path and ensure system-site-packages is enabled
-    sed -i "s|^home = .*|home = $PYTHON_DIR|" /opt/app-root/pyvenv.cfg 2>/dev/null || true && \
-    grep -q "include-system-site-packages = true" /opt/app-root/pyvenv.cfg || \
-    echo "include-system-site-packages = true" >> /opt/app-root/pyvenv.cfg && \
-    # Fix shebang paths in Python executables
-    find /opt/app-root/bin -type f -executable -exec sed -i "1s|^#!.*python.*|#!$PYTHON_PATH|" {} \; 2>/dev/null || true && \
-    rm -f /tmp/python_path.txt && \
-    # Fix ownership back to 1001:0
-    chown -R 1001:0 /opt/app-root
-
-# Switch back to unpriv user
-# Root group for k8s
-USER 1001:0
-
 ENTRYPOINT [ "/opt/app-root/bin/guidellm" ]
 CMD [ "benchmark", "run" ]
diff --git a/Containerfile.vllm b/Containerfile.vllm
new file mode 100644
index 000000000..638abfa4d
--- /dev/null
+++ b/Containerfile.vllm
@@ -0,0 +1,90 @@
+ARG BASE_IMAGE=vllm/vllm-openai
+
+FROM $BASE_IMAGE
+
+# Metadata
+LABEL io.k8s.display-name="GuideLLM" \
+      org.opencontainers.image.description="GuideLLM Performance Benchmarking Container" \
+      org.opencontainers.image.source="https://github.com/vllm-project/guidellm" \
+      org.opencontainers.image.documentation="https://blog.vllm.ai/guidellm/stable" \
+      org.opencontainers.image.license="Apache-2.0"
+
+# release: take the last version and add a post if build iteration
+# candidate: increment to next minor, add 'rc' with build iteration
+# nightly: increment to next minor, add 'a' with build iteration
+# alpha: increment to next minor, add 'a' with build iteration
+# dev: increment to next minor, add 'dev' with build iteration
+ARG GUIDELLM_BUILD_TYPE=dev
+
+# Extra dependencies to install
+# all: install all extras
+# recommended: install recommended extras
+# container: All extras except vllm
+ARG GUIDELLM_BUILD_EXTRAS=container
+
+# Switch to root for installing packages
+USER root
+
+RUN apt-get update || true \
+    && apt-get install -y --no-install-recommends gnupg ca-certificates \
+    && apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 871920D1991BC93C \
+    && apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys BA6932366A755776 \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN apt-get update && apt-get install -y git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set correct build type for versioning
+# Configure uv for building guidellm
+ENV GUIDELLM_BUILD_TYPE=$GUIDELLM_BUILD_TYPE \
+    VIRTUAL_ENV=/opt/app-root \
+    UV_PROJECT="/src" \
+    UV_LINK_MODE="copy" \
+    UV_NO_DEV="1" \
+    UV_NO_EDITABLE="1" \
+    UV_FROZEN="1" \
+    UV_CACHE_DIR="/tmp/uv_cache"
+
+# Sync initial environment
+RUN --mount=type=cache,target=$UV_CACHE_DIR \
+    --mount=type=bind,source=uv.lock,target=$UV_PROJECT/uv.lock,relabel=shared \
+    --mount=type=bind,source=pyproject.toml,target=$UV_PROJECT/pyproject.toml,relabel=shared \
+    uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS --system-site-packages
+
+# Copy repository files
+# Do this as late as possible to leverage layer caching
+COPY / $UV_PROJECT
+
+# Install guidellm
+RUN --mount=type=cache,target=$UV_CACHE_DIR \
+    uv sync --active --extra $GUIDELLM_BUILD_EXTRAS
+
+
+# Prod image
+#FROM $BASE_IMAGE
+
+# Switch to root for installing packages
+#USER root
+
+# Install some helpful utilities and deps
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        vim tar rsync ffmpeg \
+        && apt clean
+
+# Add guidellm bin to PATH
+# Argument defaults can be set with GUIDELLM_<ARG>
+ENV HOME="/home/guidellm" \
+    GUIDELLM_OUTPUT_DIR="/results"
+
+# Create the user home dir
+WORKDIR $HOME
+
+# Create a volume for results
+VOLUME /results
+
+# Switch back to unpriv user
+# Root group for k8s
+USER 1001:0
+
+ENTRYPOINT [ "/opt/app-root/bin/guidellm" ]
+CMD [ "benchmark", "run" ]

From 45dbafe792fbc0e8b0b07991bcb1bcdca8745093 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 22 Dec 2025 16:18:10 -0500
Subject: [PATCH 18/56] Fixes to containerfiles

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile.vllm |    4 +-
 pylock.toml        | 2364 +++++++++++++++++++++++++++++++++--
 uv.lock            | 2910 +++++++++++++++++++++++++++++++++++++++++---
 3 files changed, 5017 insertions(+), 261 deletions(-)

diff --git a/Containerfile.vllm b/Containerfile.vllm
index 638abfa4d..26d0070e5 100644
--- a/Containerfile.vllm
+++ b/Containerfile.vllm
@@ -46,10 +46,12 @@ ENV GUIDELLM_BUILD_TYPE=$GUIDELLM_BUILD_TYPE \
     UV_CACHE_DIR="/tmp/uv_cache"
 
 # Sync initial environment
+RUN uv venv /opt/app-root --system-site-packages
+
 RUN --mount=type=cache,target=$UV_CACHE_DIR \
     --mount=type=bind,source=uv.lock,target=$UV_PROJECT/uv.lock,relabel=shared \
     --mount=type=bind,source=pyproject.toml,target=$UV_PROJECT/pyproject.toml,relabel=shared \
-    uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS --system-site-packages
+    uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS
 
 # Copy repository files
 # Do this as late as possible to leverage layer caching
diff --git a/pylock.toml b/pylock.toml
index b0b7ce97d..1bf2a94e0 100644
--- a/pylock.toml
+++ b/pylock.toml
@@ -149,6 +149,13 @@ index = "https://pypi.org/simple"
 sdist = { url = "https://files.pythonhosted.org/packages/c9/3e/13dd8e5ed9094e734ac430b5d0eb4f2bb001708a8b7856cbf8e084e001ba/alabaster-0.7.16.tar.gz", upload-time = 2024-01-10T00:56:10Z, size = 23776, hashes = { sha256 = "75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65" } }
 wheels = [{ url = "https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl", upload-time = 2024-01-10T00:56:08Z, size = 13511, hashes = { sha256 = "b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92" } }]
 
+[[packages]]
+name = "annotated-doc"
+version = "0.0.4"
+index = "https://pypi.org/simple"
+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", upload-time = 2025-11-10T22:07:42Z, size = 7288, hashes = { sha256 = "fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4" } }
+wheels = [{ url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", upload-time = 2025-11-10T22:07:40Z, size = 5303, hashes = { sha256 = "571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320" } }]
+
 [[packages]]
 name = "annotated-types"
 version = "0.7.0"
@@ -156,6 +163,13 @@ index = "https://pypi.org/simple"
 sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", upload-time = 2024-05-20T21:33:25Z, size = 16081, hashes = { sha256 = "aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89" } }
 wheels = [{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", upload-time = 2024-05-20T21:33:24Z, size = 13643, hashes = { sha256 = "1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53" } }]
 
+[[packages]]
+name = "anthropic"
+version = "0.71.0"
+index = "https://pypi.org/simple"
+sdist = { url = "https://files.pythonhosted.org/packages/82/4f/70682b068d897841f43223df82d96ec1d617435a8b759c4a2d901a50158b/anthropic-0.71.0.tar.gz", upload-time = 2025-10-16T15:54:40Z, size = 489102, hashes = { sha256 = "eb8e6fa86d049061b3ef26eb4cbae0174ebbff21affa6de7b3098da857d8de6a" } }
+wheels = [{ url = "https://files.pythonhosted.org/packages/5d/77/073e8ac488f335aec7001952825275582fb8f433737e90f24eeef9d878f6/anthropic-0.71.0-py3-none-any.whl", upload-time = 2025-10-16T15:54:38Z, size = 355035, hashes = { sha256 = "85c5015fcdbdc728390f11b17642a65a4365d03b12b799b18b6cc57e71fdb327" } }]
+
 [[packages]]
 name = "anyio"
 version = "4.12.0"
@@ -163,6 +177,45 @@ index = "https://pypi.org/simple"
 sdist = { url = "https://files.pythonhosted.org/packages/16/ce/8a777047513153587e5434fd752e89334ac33e379aa3497db860eeb60377/anyio-4.12.0.tar.gz", upload-time = 2025-11-28T23:37:38Z, size = 228266, hashes = { sha256 = "73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0" } }
 wheels = [{ url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", upload-time = 2025-11-28T23:36:57Z, size = 113362, hashes = { sha256 = "dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb" } }]
 
+[[packages]]
+name = "apache-tvm-ffi"
+version = "0.1.6"
+index = "https://pypi.org/simple"
+sdist = { url = "https://files.pythonhosted.org/packages/45/20/8da071821b2142bdeed757d2859dede4817e0b82a96e9a4d8cfbffd49006/apache_tvm_ffi-0.1.6.tar.gz", upload-time = 2025-12-16T19:00:33Z, size = 2387987, hashes = { sha256 = "53088126f7fce11823ddf0fb101e968a90298d79fd68829c0a981f25467a574c" } }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c7/f8/6bc29ca8945a8a0b52997fd1e564c783f5b2578b6125315ed30dd0b1d0e4/apache_tvm_ffi-0.1.6-cp310-cp310-macosx_11_0_arm64.whl", upload-time = 2025-12-16T18:59:40Z, size = 1806165, hashes = { sha256 = "ecda748ad9139593296cde3581223e9ddf1be3feca987adea676708b98f297ac" } },
+    { url = "https://files.pythonhosted.org/packages/1c/12/310a9953d6a35c2975e0d585f5bdd936858ec6b5b9daee34dc49dd4e3e2e/apache_tvm_ffi-0.1.6-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", upload-time = 2025-12-16T18:59:42Z, size = 1965883, hashes = { sha256 = "d976e347d0e6f6695103ce90cc739c717b3623fb9fd4867ffc395e2fe006f345" } },
+    { url = "https://files.pythonhosted.org/packages/9a/e1/37326821f2976167f142d23ded0e80f15ca05408ab49d87a2151ff246c76/apache_tvm_ffi-0.1.6-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", upload-time = 2025-12-16T18:59:43Z, size = 2037885, hashes = { sha256 = "e6caf9fdc209c3a6f618a462fc8c0925525246f16912f6333424819f19484c06" } },
+    { url = "https://files.pythonhosted.org/packages/28/d2/614d397d69b20ccf86d07f3e02d77e0056415f82e81816905ae1d11cd6e5/apache_tvm_ffi-0.1.6-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", upload-time = 2025-12-16T18:59:45Z, size = 1909586, hashes = { sha256 = "d43d8540bc38eb7f5173f8516a7963b2b0a8cdbc3fe315600d856fe2e3ed0f6f" } },
+    { url = "https://files.pythonhosted.org/packages/1c/3a/79aac72fbf67aac585757d34a57770d17c0ee34e9e46f668ab62df5c16ce/apache_tvm_ffi-0.1.6-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", upload-time = 2025-12-16T18:59:46Z, size = 2019954, hashes = { sha256 = "f08cb6638dd2cd2e9f1cdc5126be676632ecaf09edb1ad6d43f836baa2f02845" } },
+    { url = "https://files.pythonhosted.org/packages/73/99/857e1497bfec2e3622ec21ca706b9af6f2ec94bca162d1216855cc617752/apache_tvm_ffi-0.1.6-cp310-cp310-win_amd64.whl", upload-time = 2025-12-16T18:59:47Z, size = 1777727, hashes = { sha256 = "017576fc9a638a37cb2fc7024a3b2f9071a54db62545daf166efc8f9c8fda8a3" } },
+    { url = "https://files.pythonhosted.org/packages/a6/d1/dc4878dcca3d244918fa815a00c558652209f68a1678280b01cd79cdcc01/apache_tvm_ffi-0.1.6-cp311-cp311-macosx_11_0_arm64.whl", upload-time = 2025-12-16T18:59:49Z, size = 1807748, hashes = { sha256 = "52e9213b553e729e9bcf9acb2bfa0d7e3000fc4756f86ed375827b1e4b53692f" } },
+    { url = "https://files.pythonhosted.org/packages/fb/44/9e33ca98ee36f1ddf81246d8aad64a87728e03590dae71f3a99b8647c853/apache_tvm_ffi-0.1.6-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", upload-time = 2025-12-16T18:59:51Z, size = 1965166, hashes = { sha256 = "9532d721f208e4b9989f0e1b3a2d785c6b26d27d3e2b378b945c60d9c29e86ce" } },
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 name = "guidellm"
 source = { editable = "." }
@@ -802,8 +1590,7 @@ dependencies = [
     { name = "httpx", extra = ["http2"] },
     { name = "loguru" },
     { name = "msgpack" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "protobuf" },
     { name = "pydantic" },
     { name = "pydantic-settings" },
@@ -811,9 +1598,6 @@ dependencies = [
     { name = "rich" },
     { name = "sanic" },
     { name = "tabulate" },
-    { name = "torch", version = "2.9.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.9.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
-    { name = "transformers" },
     { name = "uvloop" },
 ]
 
@@ -826,17 +1610,28 @@ all = [
     { name = "orjson" },
     { name = "pillow" },
     { name = "tiktoken" },
-    { name = "torch", version = "2.9.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.9.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
+    { name = "torch", version = "2.9.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.9.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
     { name = "torchcodec" },
+    { name = "transformers" },
     { name = "uvloop" },
+    { name = "vllm" },
 ]
 audio = [
     { name = "datasets", extra = ["audio"] },
-    { name = "torch", version = "2.9.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.9.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
     { name = "torchcodec" },
 ]
+container = [
+    { name = "blobfile" },
+    { name = "datasets", extra = ["audio", "vision"] },
+    { name = "msgpack" },
+    { name = "msgspec" },
+    { name = "orjson" },
+    { name = "pillow" },
+    { name = "tiktoken" },
+    { name = "torchcodec" },
+    { name = "uvloop" },
+]
 dev = [
     { name = "blobfile" },
     { name = "build" },
@@ -868,14 +1663,16 @@ dev = [
     { name = "setuptools-git-versioning" },
     { name = "sphinx" },
     { name = "tiktoken" },
-    { name = "torch", version = "2.9.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.9.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
+    { name = "torch", version = "2.9.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.9.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
     { name = "torchcodec" },
     { name = "tox" },
+    { name = "transformers" },
     { name = "types-pyyaml" },
     { name = "types-requests" },
     { name = "types-toml" },
     { name = "uvloop" },
+    { name = "vllm" },
 ]
 openai = [
     { name = "blobfile" },
@@ -887,18 +1684,29 @@ perf = [
     { name = "orjson" },
     { name = "uvloop" },
 ]
+pytorch = [
+    { name = "torch", version = "2.9.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.9.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
+    { name = "transformers" },
+]
 recommended = [
     { name = "blobfile" },
     { name = "msgpack" },
     { name = "msgspec" },
     { name = "orjson" },
     { name = "tiktoken" },
+    { name = "torch", version = "2.9.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.9.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
+    { name = "transformers" },
     { name = "uvloop" },
 ]
 vision = [
     { name = "datasets", extra = ["vision"] },
     { name = "pillow" },
 ]
+vllm = [
+    { name = "vllm" },
+]
 
 [package.dev-dependencies]
 dev = [
@@ -918,8 +1726,9 @@ requires-dist = [
     { name = "faker" },
     { name = "ftfy", specifier = ">=6.0.0" },
     { name = "guidellm", extras = ["all"], marker = "extra == 'dev'" },
-    { name = "guidellm", extras = ["audio", "openai", "perf", "vision"], marker = "extra == 'all'" },
-    { name = "guidellm", extras = ["openai", "perf"], marker = "extra == 'recommended'" },
+    { name = "guidellm", extras = ["audio", "openai", "perf", "pytorch", "vision", "vllm"], marker = "extra == 'all'" },
+    { name = "guidellm", extras = ["audio", "openai", "perf", "vision"], marker = "extra == 'container'" },
+    { name = "guidellm", extras = ["openai", "perf", "pytorch"], marker = "extra == 'recommended'" },
     { name = "httpx", extras = ["http2"], specifier = "<1.0.0" },
     { name = "loguru" },
     { name = "lorem", marker = "extra == 'dev'", specifier = "~=0.1.1" },
@@ -957,18 +1766,18 @@ requires-dist = [
     { name = "sphinx", marker = "extra == 'dev'", specifier = "~=7.1.2" },
     { name = "tabulate" },
     { name = "tiktoken", marker = "extra == 'openai'", specifier = ">=0.11.0" },
-    { name = "torch", index = "https://download.pytorch.org/whl/cpu" },
-    { name = "torch", marker = "extra == 'audio'", specifier = "==2.9.*", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "torch", marker = "extra == 'pytorch'", index = "https://download.pytorch.org/whl/cpu" },
     { name = "torchcodec", marker = "extra == 'audio'", specifier = "==0.8.*" },
     { name = "tox", marker = "extra == 'dev'", specifier = "~=4.16.0" },
-    { name = "transformers" },
+    { name = "transformers", marker = "extra == 'pytorch'" },
     { name = "types-pyyaml", marker = "extra == 'dev'", specifier = "~=6.0.1" },
     { name = "types-requests", marker = "extra == 'dev'", specifier = "~=2.32.0" },
     { name = "types-toml", marker = "extra == 'dev'" },
     { name = "uvloop", specifier = ">=0.18" },
     { name = "uvloop", marker = "extra == 'perf'" },
+    { name = "vllm", marker = "extra == 'vllm'", specifier = ">=0.6.0" },
 ]
-provides-extras = ["all", "recommended", "perf", "openai", "audio", "vision", "dev"]
+provides-extras = ["all", "recommended", "container", "perf", "openai", "pytorch", "vllm", "audio", "vision", "dev"]
 
 [package.metadata.requires-dev]
 dev = [{ name = "guidellm", extras = ["dev"] }]
@@ -1118,6 +1927,15 @@ http2 = [
     { name = "h2" },
 ]
 
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+]
+
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 name = "huggingface-hub"
 version = "0.36.0"
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 ]
 
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 name = "wcwidth"
 version = "0.2.14"
@@ -4223,6 +6800,43 @@ wheels = [
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 ]
 
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+    { name = "numpy" },
+    { name = "pydantic" },
+    { name = "torch", version = "2.9.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.9.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
+    { name = "transformers" },
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+]
+
 [[package]]
 name = "xxhash"
 version = "3.6.0"

From 5526048c94c234ae7a30e9b1a41eda27b2f52e31 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 22 Dec 2025 17:27:28 -0500
Subject: [PATCH 19/56] Fix torchvision

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 pyproject.toml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 815cd7840..284879cce 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,7 @@ explicit = true
 
 [tool.uv.sources]
 torch = { index = "pytorch-cpu" }
+torchvision = { index = "pytorch-cpu" }
 
 # ************************************************
 # ********** Project Metadata **********
@@ -90,6 +91,7 @@ audio = [
 vision = [
     "datasets[vision]",
     "pillow",
+    "torchvision",
 ]
 # Dev Tooling
 dev = [

From 2d6965631d60afa214f408f9a9db123542d2c5e1 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 22 Dec 2025 17:39:45 -0500
Subject: [PATCH 20/56] Revert "Fixes to containerfiles"

This reverts commit 45dbafe792fbc0e8b0b07991bcb1bcdca8745093.
---
 Containerfile.vllm |    4 +-
 pylock.toml        | 2364 ++---------------------------------
 uv.lock            | 2952 +++-----------------------------------------
 3 files changed, 282 insertions(+), 5038 deletions(-)

diff --git a/Containerfile.vllm b/Containerfile.vllm
index 26d0070e5..638abfa4d 100644
--- a/Containerfile.vllm
+++ b/Containerfile.vllm
@@ -46,12 +46,10 @@ ENV GUIDELLM_BUILD_TYPE=$GUIDELLM_BUILD_TYPE \
     UV_CACHE_DIR="/tmp/uv_cache"
 
 # Sync initial environment
-RUN uv venv /opt/app-root --system-site-packages
-
 RUN --mount=type=cache,target=$UV_CACHE_DIR \
     --mount=type=bind,source=uv.lock,target=$UV_PROJECT/uv.lock,relabel=shared \
     --mount=type=bind,source=pyproject.toml,target=$UV_PROJECT/pyproject.toml,relabel=shared \
-    uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS
+    uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS --system-site-packages
 
 # Copy repository files
 # Do this as late as possible to leverage layer caching
diff --git a/pylock.toml b/pylock.toml
index 1bf2a94e0..b0b7ce97d 100644
--- a/pylock.toml
+++ b/pylock.toml
@@ -149,13 +149,6 @@ index = "https://pypi.org/simple"
 sdist = { url = "https://files.pythonhosted.org/packages/c9/3e/13dd8e5ed9094e734ac430b5d0eb4f2bb001708a8b7856cbf8e084e001ba/alabaster-0.7.16.tar.gz", upload-time = 2024-01-10T00:56:10Z, size = 23776, hashes = { sha256 = "75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65" } }
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-[[packages]]
-name = "annotated-doc"
-version = "0.0.4"
-index = "https://pypi.org/simple"
-sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", upload-time = 2025-11-10T22:07:42Z, size = 7288, hashes = { sha256 = "fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4" } }
-wheels = [{ url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", upload-time = 2025-11-10T22:07:40Z, size = 5303, hashes = { sha256 = "571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320" } }]
-
 [[packages]]
 name = "annotated-types"
 version = "0.7.0"
@@ -163,13 +156,6 @@ index = "https://pypi.org/simple"
 sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", upload-time = 2024-05-20T21:33:25Z, size = 16081, hashes = { sha256 = "aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89" } }
 wheels = [{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", upload-time = 2024-05-20T21:33:24Z, size = 13643, hashes = { sha256 = "1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53" } }]
 
-[[packages]]
-name = "anthropic"
-version = "0.71.0"
-index = "https://pypi.org/simple"
-sdist = { url = "https://files.pythonhosted.org/packages/82/4f/70682b068d897841f43223df82d96ec1d617435a8b759c4a2d901a50158b/anthropic-0.71.0.tar.gz", upload-time = 2025-10-16T15:54:40Z, size = 489102, hashes = { sha256 = "eb8e6fa86d049061b3ef26eb4cbae0174ebbff21affa6de7b3098da857d8de6a" } }
-wheels = [{ url = "https://files.pythonhosted.org/packages/5d/77/073e8ac488f335aec7001952825275582fb8f433737e90f24eeef9d878f6/anthropic-0.71.0-py3-none-any.whl", upload-time = 2025-10-16T15:54:38Z, size = 355035, hashes = { sha256 = "85c5015fcdbdc728390f11b17642a65a4365d03b12b799b18b6cc57e71fdb327" } }]
-
 [[packages]]
 name = "anyio"
 version = "4.12.0"
@@ -177,45 +163,6 @@ index = "https://pypi.org/simple"
 sdist = { url = "https://files.pythonhosted.org/packages/16/ce/8a777047513153587e5434fd752e89334ac33e379aa3497db860eeb60377/anyio-4.12.0.tar.gz", upload-time = 2025-11-28T23:37:38Z, size = 228266, hashes = { sha256 = "73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0" } }
 wheels = [{ url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", upload-time = 2025-11-28T23:36:57Z, size = 113362, hashes = { sha256 = "dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb" } }]
 
-[[packages]]
-name = "apache-tvm-ffi"
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@@ -1590,7 +802,8 @@ dependencies = [
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     { name = "loguru" },
     { name = "msgpack" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "protobuf" },
     { name = "pydantic" },
     { name = "pydantic-settings" },
@@ -1598,6 +811,9 @@ dependencies = [
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     { name = "sanic" },
     { name = "tabulate" },
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+    { name = "torch", version = "2.9.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
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-    { name = "transformers" },
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-    { name = "vllm" },
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+    { name = "torch", version = "2.9.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.9.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
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@@ -1663,16 +868,14 @@ dev = [
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-    { name = "torch", version = "2.9.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.9.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
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@@ -1684,29 +887,18 @@ perf = [
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@@ -1726,9 +918,8 @@ requires-dist = [
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-    { name = "guidellm", extras = ["openai", "perf", "pytorch"], marker = "extra == 'recommended'" },
+    { name = "guidellm", extras = ["audio", "openai", "perf", "vision"], marker = "extra == 'all'" },
+    { name = "guidellm", extras = ["openai", "perf"], marker = "extra == 'recommended'" },
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     { name = "loguru" },
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@@ -1766,18 +957,18 @@ requires-dist = [
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-    { name = "torch", marker = "extra == 'pytorch'", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "torch", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "torch", marker = "extra == 'audio'", specifier = "==2.9.*", index = "https://download.pytorch.org/whl/cpu" },
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     { name = "tox", marker = "extra == 'dev'", specifier = "~=4.16.0" },
-    { name = "transformers", marker = "extra == 'pytorch'" },
+    { name = "transformers" },
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+provides-extras = ["all", "recommended", "perf", "openai", "audio", "vision", "dev"]
 
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@@ -1927,15 +1118,6 @@ http2 = [
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 [[package]]
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 version = "3.6.0"

From ea7b81b128466b6fa7c661c3a447e69599d36fc0 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 22 Dec 2025 17:44:28 -0500
Subject: [PATCH 21/56] Support CUDA in the containerfile

Generated-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile | 26 +++++++++++++++++++++++++-
 1 file changed, 25 insertions(+), 1 deletion(-)

diff --git a/Containerfile b/Containerfile
index 558c2ed9f..7330a6d80 100644
--- a/Containerfile
+++ b/Containerfile
@@ -16,6 +16,14 @@ ARG GUIDELLM_BUILD_TYPE=dev
 # container: All extras except vllm
 ARG GUIDELLM_BUILD_EXTRAS=container
 
+# PyTorch CUDA version selection
+# cpu: Use CPU-only PyTorch builds (default)
+# cu121: Use CUDA 12.1 builds
+# cu118: Use CUDA 11.8 builds
+# Other CUDA versions: cu124, cu131, etc.
+# Note: Non-CPU builds will resolve dependencies at build time (no frozen lock file)
+ARG PYTORCH_CUDA_VERSION=cpu
+
 # Switch to root for installing packages
 USER root
 
@@ -30,16 +38,32 @@ RUN --mount=type=cache,sharing=locked,target=/var/cache/dnf \
 
 # Set correct build type for versioning
 # Configure uv for building guidellm
+# UV_FROZEN is only set for CPU builds to use the lock file
+# CUDA builds resolve dependencies at build time for flexibility
 ENV GUIDELLM_BUILD_TYPE=$GUIDELLM_BUILD_TYPE \
     VIRTUAL_ENV=/opt/app-root \
     UV_PROJECT="/src" \
     UV_LINK_MODE="copy" \
     UV_NO_DEV="1" \
     UV_NO_EDITABLE="1" \
-    UV_FROZEN="1" \
+    UV_FROZEN=$([ "$PYTORCH_CUDA_VERSION" = "cpu" ] && echo "1" || echo "") \
     UV_CACHE_DIR="/tmp/uv_cache"
 
+# Conditionally configure PyTorch index based on CUDA version
+# This modifies the pyproject.toml to use the appropriate PyTorch index URL
+RUN --mount=type=bind,source=pyproject.toml,target=$UV_PROJECT/pyproject.toml,relabel=shared \
+    if [ "$PYTORCH_CUDA_VERSION" != "cpu" ]; then \
+        sed -i "s|url = \"https://download.pytorch.org/whl/cpu\"|url = \"https://download.pytorch.org/whl/${PYTORCH_CUDA_VERSION}\"|g" \
+        $UV_PROJECT/pyproject.toml && \
+        echo "Updated PyTorch index to CUDA ${PYTORCH_CUDA_VERSION}"; \
+    else \
+        echo "Using CPU-only PyTorch index"; \
+    fi
+
 # Sync initial environment
+# For CPU builds: mount uv.lock and use frozen mode (UV_FROZEN=1) for reproducibility
+# For CUDA builds: mount lock file but it won't be used since UV_FROZEN is empty,
+#                  allowing dependencies to resolve at build time
 RUN --mount=type=cache,target=$UV_CACHE_DIR \
     --mount=type=bind,source=uv.lock,target=$UV_PROJECT/uv.lock,relabel=shared \
     --mount=type=bind,source=pyproject.toml,target=$UV_PROJECT/pyproject.toml,relabel=shared \

From 2f39e9ed106e29eade629a3628529ccdc6741013 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 22 Dec 2025 17:46:39 -0500
Subject: [PATCH 22/56] Fix errors

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/Containerfile b/Containerfile
index 7330a6d80..26694991f 100644
--- a/Containerfile
+++ b/Containerfile
@@ -46,7 +46,6 @@ ENV GUIDELLM_BUILD_TYPE=$GUIDELLM_BUILD_TYPE \
     UV_LINK_MODE="copy" \
     UV_NO_DEV="1" \
     UV_NO_EDITABLE="1" \
-    UV_FROZEN=$([ "$PYTORCH_CUDA_VERSION" = "cpu" ] && echo "1" || echo "") \
     UV_CACHE_DIR="/tmp/uv_cache"
 
 # Conditionally configure PyTorch index based on CUDA version
@@ -61,13 +60,16 @@ RUN --mount=type=bind,source=pyproject.toml,target=$UV_PROJECT/pyproject.toml,re
     fi
 
 # Sync initial environment
-# For CPU builds: mount uv.lock and use frozen mode (UV_FROZEN=1) for reproducibility
-# For CUDA builds: mount lock file but it won't be used since UV_FROZEN is empty,
-#                  allowing dependencies to resolve at build time
+# For CPU builds: mount uv.lock and use frozen mode (--frozen flag) for reproducibility
+# For CUDA builds: mount lock file but don't use --frozen, allowing dependencies to resolve at build time
 RUN --mount=type=cache,target=$UV_CACHE_DIR \
     --mount=type=bind,source=uv.lock,target=$UV_PROJECT/uv.lock,relabel=shared \
     --mount=type=bind,source=pyproject.toml,target=$UV_PROJECT/pyproject.toml,relabel=shared \
-    uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS
+    if [ "$PYTORCH_CUDA_VERSION" = "cpu" ]; then \
+        uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS --frozen; \
+    else \
+        uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS; \
+    fi
 
 # Copy repository files
 # Do this as late as possible to leverage layer caching
@@ -75,7 +77,11 @@ COPY / $UV_PROJECT
 
 # Install guidellm
 RUN --mount=type=cache,target=$UV_CACHE_DIR \
-    uv sync --active --extra $GUIDELLM_BUILD_EXTRAS
+    if [ "$PYTORCH_CUDA_VERSION" = "cpu" ]; then \
+        uv sync --active --extra $GUIDELLM_BUILD_EXTRAS --frozen; \
+    else \
+        uv sync --active --extra $GUIDELLM_BUILD_EXTRAS; \
+    fi
 
 # Prod image
 FROM $BASE_IMAGE

From 5223f3d1422780162f180d03f44f47c567411e9d Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 22 Dec 2025 18:09:25 -0500
Subject: [PATCH 23/56] Update script to include extras

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 scripts/generate_pylock.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/generate_pylock.sh b/scripts/generate_pylock.sh
index caf2ee2af..d7951d503 100755
--- a/scripts/generate_pylock.sh
+++ b/scripts/generate_pylock.sh
@@ -27,9 +27,9 @@ while getopts "fh" opt; do
 done
 
 if [ $FORCE_REGEN -eq 1 ]; then
-    uv lock -U
+    uv sync --all-extras --no-install-project --upgrade
 else
-    uv lock
+    uv sync --all-extras --no-install-project
 fi
 # NOTE: Ensure pre-commit config uses same arguments
 uv export \

From 1838ef56e89d27a02035c02c872a37e472d56993 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Tue, 13 Jan 2026 17:39:46 -0500
Subject: [PATCH 24/56] Revert Containerfile to main's version

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile | 39 ++++-----------------------------------
 1 file changed, 4 insertions(+), 35 deletions(-)

diff --git a/Containerfile b/Containerfile
index 26694991f..ceb5e074d 100644
--- a/Containerfile
+++ b/Containerfile
@@ -13,16 +13,7 @@ ARG GUIDELLM_BUILD_TYPE=dev
 # Extra dependencies to install
 # all: install all extras
 # recommended: install recommended extras
-# container: All extras except vllm
-ARG GUIDELLM_BUILD_EXTRAS=container
-
-# PyTorch CUDA version selection
-# cpu: Use CPU-only PyTorch builds (default)
-# cu121: Use CUDA 12.1 builds
-# cu118: Use CUDA 11.8 builds
-# Other CUDA versions: cu124, cu131, etc.
-# Note: Non-CPU builds will resolve dependencies at build time (no frozen lock file)
-ARG PYTORCH_CUDA_VERSION=cpu
+ARG GUIDELLM_BUILD_EXTRAS=all
 
 # Switch to root for installing packages
 USER root
@@ -38,38 +29,20 @@ RUN --mount=type=cache,sharing=locked,target=/var/cache/dnf \
 
 # Set correct build type for versioning
 # Configure uv for building guidellm
-# UV_FROZEN is only set for CPU builds to use the lock file
-# CUDA builds resolve dependencies at build time for flexibility
 ENV GUIDELLM_BUILD_TYPE=$GUIDELLM_BUILD_TYPE \
     VIRTUAL_ENV=/opt/app-root \
     UV_PROJECT="/src" \
     UV_LINK_MODE="copy" \
     UV_NO_DEV="1" \
     UV_NO_EDITABLE="1" \
+    UV_FROZEN="1" \
     UV_CACHE_DIR="/tmp/uv_cache"
 
-# Conditionally configure PyTorch index based on CUDA version
-# This modifies the pyproject.toml to use the appropriate PyTorch index URL
-RUN --mount=type=bind,source=pyproject.toml,target=$UV_PROJECT/pyproject.toml,relabel=shared \
-    if [ "$PYTORCH_CUDA_VERSION" != "cpu" ]; then \
-        sed -i "s|url = \"https://download.pytorch.org/whl/cpu\"|url = \"https://download.pytorch.org/whl/${PYTORCH_CUDA_VERSION}\"|g" \
-        $UV_PROJECT/pyproject.toml && \
-        echo "Updated PyTorch index to CUDA ${PYTORCH_CUDA_VERSION}"; \
-    else \
-        echo "Using CPU-only PyTorch index"; \
-    fi
-
 # Sync initial environment
-# For CPU builds: mount uv.lock and use frozen mode (--frozen flag) for reproducibility
-# For CUDA builds: mount lock file but don't use --frozen, allowing dependencies to resolve at build time
 RUN --mount=type=cache,target=$UV_CACHE_DIR \
     --mount=type=bind,source=uv.lock,target=$UV_PROJECT/uv.lock,relabel=shared \
     --mount=type=bind,source=pyproject.toml,target=$UV_PROJECT/pyproject.toml,relabel=shared \
-    if [ "$PYTORCH_CUDA_VERSION" = "cpu" ]; then \
-        uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS --frozen; \
-    else \
-        uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS; \
-    fi
+    uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS
 
 # Copy repository files
 # Do this as late as possible to leverage layer caching
@@ -77,11 +50,7 @@ COPY / $UV_PROJECT
 
 # Install guidellm
 RUN --mount=type=cache,target=$UV_CACHE_DIR \
-    if [ "$PYTORCH_CUDA_VERSION" = "cpu" ]; then \
-        uv sync --active --extra $GUIDELLM_BUILD_EXTRAS --frozen; \
-    else \
-        uv sync --active --extra $GUIDELLM_BUILD_EXTRAS; \
-    fi
+    uv sync --active --extra $GUIDELLM_BUILD_EXTRAS
 
 # Prod image
 FROM $BASE_IMAGE

From 0f86fac3c37a03178182ef64a394febdbddfd0ab Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Fri, 6 Feb 2026 19:16:49 -0500
Subject: [PATCH 25/56] Updated vLLM python backend to use new backend design

Generated-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/__main__.py                      |   7 +-
 src/guidellm/backends/__init__.py             |   8 +-
 src/guidellm/backends/vllm_python/__init__.py |  10 +
 .../backends/{ => vllm_python}/vllm.py        | 292 +++++++++++-------
 .../backends/vllm_python/vllm_response.py     | 216 +++++++++++++
 .../schemas/generative/entrypoints.py         |   5 +-
 6 files changed, 420 insertions(+), 118 deletions(-)
 create mode 100644 src/guidellm/backends/vllm_python/__init__.py
 rename src/guidellm/backends/{ => vllm_python}/vllm.py (76%)
 create mode 100644 src/guidellm/backends/vllm_python/vllm_response.py

diff --git a/src/guidellm/__main__.py b/src/guidellm/__main__.py
index c953972c3..6e562450f 100644
--- a/src/guidellm/__main__.py
+++ b/src/guidellm/__main__.py
@@ -190,7 +190,12 @@ def benchmark():
     default=BenchmarkGenerativeTextArgs.get_default("request_format"),
     help=(
         "Format to use for requests. Options depend on backend. "
-        "If not provided, uses backend default."
+        "For vLLM backend: plain (no chat template, text appending only), "
+        "default-template (use tokenizer default), or a file path / single-line "
+        "template per vLLM docs. Default: default-template"
+        "For openai backend: http endpoint path (/v1/chat/completions, "
+        "/v1/completions, /v1/audio/transcriptions, /v1/audio/translations) or "
+        "alias (e.g. chat_completions); default /v1/chat/completions."
     ),
 )
 @click.option(
diff --git a/src/guidellm/backends/__init__.py b/src/guidellm/backends/__init__.py
index 953d9d6f2..13a5f580f 100644
--- a/src/guidellm/backends/__init__.py
+++ b/src/guidellm/backends/__init__.py
@@ -21,11 +21,13 @@
 
 # Conditionally import VLLM backend if available
 try:
-    from .vllm import VLLMPythonBackend
+    from .vllm_python.vllm import VLLMPythonBackend
+    from .vllm_python.vllm_response import VLLMResponseHandler
 
     HAS_VLLM_BACKEND = True
 except ImportError:
     VLLMPythonBackend = None  # type: ignore[assignment, misc]
+    VLLMResponseHandler = None  # type: ignore[assignment, misc]
     HAS_VLLM_BACKEND = False
 
 __all__ = [
@@ -39,6 +41,6 @@
     "TextCompletionsRequestHandler",
 ]
 
-# Conditionally add VLLM backend to exports
+# Conditionally add VLLM backend and handler to exports
 if HAS_VLLM_BACKEND:
-    __all__.append("VLLMPythonBackend")
+    __all__.extend(["VLLMPythonBackend", "VLLMResponseHandler"])
diff --git a/src/guidellm/backends/vllm_python/__init__.py b/src/guidellm/backends/vllm_python/__init__.py
new file mode 100644
index 000000000..b69bb9d9f
--- /dev/null
+++ b/src/guidellm/backends/vllm_python/__init__.py
@@ -0,0 +1,10 @@
+"""
+VLLM Python API backend package.
+
+Provides the VLLM Python backend and response handler for compiling
+OpenAI-style response dicts into GenerationResponse.
+"""
+
+from .vllm_response import VLLMResponseHandler
+
+__all__ = ["VLLMResponseHandler"]
diff --git a/src/guidellm/backends/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
similarity index 76%
rename from src/guidellm/backends/vllm.py
rename to src/guidellm/backends/vllm_python/vllm.py
index ec8fccc87..6e930bc1f 100644
--- a/src/guidellm/backends/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -12,8 +12,11 @@
 import asyncio
 import time
 import uuid
+from pathlib import Path
 from collections.abc import AsyncIterator
-from typing import Any
+from typing import Any, Literal
+
+_Mode = Literal["audio", "chat", "text"]
 
 import numpy as np
 
@@ -32,7 +35,7 @@
     HAS_VLLM = False
 
 from guidellm.backends.backend import Backend
-from guidellm.backends.response_handlers import GenerationResponseHandlerFactory
+from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler
 from guidellm.extras.audio import _decode_audio
 from guidellm.schemas import GenerationRequest, GenerationResponse, RequestInfo, UsageMetrics
 from guidellm.utils import json
@@ -40,6 +43,24 @@
 __all__ = ["VLLMPythonBackend"]
 
 
+class _RequestContext:
+    """Resolved mode, body, stream, and files for a generation request."""
+
+    __slots__ = ("mode", "body", "stream", "files")
+
+    def __init__(
+        self,
+        mode: _Mode,
+        body: dict[str, Any],
+        stream: bool,
+        files: dict[str, Any],
+    ) -> None:
+        self.mode = mode
+        self.body = body
+        self.stream = stream
+        self.files = files
+
+
 def _check_vllm_available() -> None:
     """Check if vllm is available and raise helpful error if not."""
     if not HAS_VLLM:
@@ -108,8 +129,8 @@ def __init__(
         self,
         model: str,
         vllm_config: dict[str, Any] | None = None,
+        request_format: str | None = None,
         target: str | None = None,  # Ignored for VLLM Python backend
-        response_handlers: dict[str, Any] | None = None,
     ):
         """
         Initialize VLLM Python backend with model and configuration.
@@ -124,11 +145,15 @@ def __init__(
             Note: VLLM automatically uses CUDA if available, CPU otherwise.
             The 'device' parameter is not supported and will be ignored.
         :param target: Target URL (ignored for VLLM Python backend, which runs locally)
+        :param kwargs: Additional arguments from the benchmark. request_format can be
+            "plain" (no chat template, text appending only), "default-template" (use
+            tokenizer default), or a vLLM chat template file path / single-line string.
         """
         _check_vllm_available()
         super().__init__(type_="vllm_python")
 
         self.model = model
+        self.request_format = request_format
         self.vllm_config = self._merge_config(vllm_config or {})
 
         # Runtime state
@@ -146,6 +171,9 @@ def _merge_config(self, user_config: dict[str, Any]) -> dict[str, Any]:
         """
         Merge user configuration with defaults.
 
+        When request_format is a custom template (not plain or default-template),
+        adds chat_template to config for the vLLM engine if supported.
+
         :param user_config: User-provided configuration dictionary
         :return: Merged configuration with defaults applied
         """
@@ -157,6 +185,13 @@ def _merge_config(self, user_config: dict[str, Any]) -> dict[str, Any]:
         # Ensure model is set in config
         config["model"] = self.model
 
+        # Pass custom chat template to vLLM engine when applicable
+        if (
+            self.request_format is not None
+            and self.request_format not in ("plain", "default-template")
+        ):
+            config["chat_template"] = self.request_format
+
         return config
 
     @property
@@ -266,25 +301,53 @@ def _validate_history(
         if history is not None:
             raise NotImplementedError("Multi-turn requests not yet supported")
 
-    def _validate_request_type(self, request_type: str) -> None:
+    def _get_request_context(self, request: GenerationRequest) -> _RequestContext:
         """
-        Validate that the request type is supported.
+        Resolve body, stream, and files from request; infer mode from that data.
 
-        :param request_type: The request type to validate
-        :raises ValueError: If request type is not supported
+        When the request has arguments, use body/stream/files from them. Otherwise
+        build body from request.columns (always messages) so the standard benchmark
+        pipeline works. Mode is inferred from body and files (audio/chat/text).
         """
-        supported_types = (
-            "text_completions",
-            "chat_completions",
-            "audio_transcriptions",
-            "audio_translations",
-        )
-        if request_type not in supported_types:
+        arguments = getattr(request, "arguments", None)
+
+        if arguments is not None:
+            body = getattr(arguments, "body", None) or {}
+            stream = bool(getattr(arguments, "stream", False))
+            files = getattr(arguments, "files", None) or {}
+        else:
+            columns = getattr(request, "columns", {}) or {}
+            prefix_parts = list(columns.get("prefix_column", []) or [])
+            text_parts = list(columns.get("text_column", []) or [])
+            messages: list[dict[str, Any]] = []
+            for p in prefix_parts:
+                if p:
+                    messages.append({"role": "system", "content": str(p)})
+            for t in text_parts:
+                if t:
+                    messages.append({
+                        "role": "user",
+                        "content": [{"type": "text", "text": str(t)}],
+                    })
+            body = {"messages": messages}
+            stream = False
+            files = {}
+
+        if files:
+            mode: _Mode = "audio"
+        elif body.get("messages"):
+            mode = "chat"
+        elif body.get("prompt") is not None and body.get("prompt") != "":
+            mode = "text"
+        elif "prompt" in body:
+            mode = "text"
+        else:
             raise ValueError(
-                f"Unsupported request type '{request_type}'. "
-                f"Supported types are: {', '.join(supported_types)}."
+                "Request must include prompt, messages, or audio files."
             )
 
+        return _RequestContext(mode=mode, body=body, stream=stream, files=files)
+
     def _update_request_timing(self, request_info: RequestInfo, iter_time: float) -> None:
         """
         Update request iteration timing information.
@@ -341,95 +404,83 @@ def _extract_text_from_content(self, content: str | list[dict[str, Any]] | Any)
         # Fallback: convert to string
         return str(content) if content is not None else ""
 
-    def _extract_audio_from_request(self, request: GenerationRequest) -> tuple[bytes, str] | None:
+    def _extract_audio_from_request(
+        self, files: dict[str, Any]
+    ) -> tuple[bytes, str]:
         """
-        Extract audio file from generation request.
+        Extract audio file from request files dict.
 
-        Extracts audio file data and mimetype from request.arguments.files.
-        Audio files are expected to be in the format: (filename, bytes, mimetype).
+        Caller must only call when mode is audio. Audio files are expected in
+        the format (filename, bytes, mimetype) or as raw bytes.
 
-        :param request: Generation request with files containing audio data
-        :return: Tuple of (audio_bytes, mimetype) or None if no audio file found
-        :raises ValueError: If audio file is missing or invalid for audio request types
+        :param files: Files dict from request context
+        :return: Tuple of (audio_bytes, mimetype)
+        :raises ValueError: If files is empty or no valid audio file found
         """
-        if request.request_type not in ("audio_transcriptions", "audio_translations"):
-            return None
-
-        files = request.arguments.files or {}
         if not files:
             raise ValueError(
-                f"{request.request_type} request must include audio file in 'files'"
+                "Audio request must include audio file in 'files'."
             )
 
-        # Find the audio file (typically keyed as "file")
         audio_file = None
-        for key, value in files.items():
+        for value in files.values():
             if isinstance(value, (list, tuple)) and len(value) >= 2:
-                # Format: (filename, bytes, mimetype) or [filename, bytes, mimetype]
                 audio_file = value
                 break
             elif isinstance(value, bytes):
-                # Direct bytes, use default filename and mimetype
                 audio_file = ("audio.wav", value, "audio/wav")
                 break
 
         if audio_file is None:
             raise ValueError(
-                f"{request.request_type} request must include audio file in 'files'"
+                "Audio request must include audio file in 'files'."
             )
 
-        # Extract bytes and mimetype
-        if isinstance(audio_file, (list, tuple)):
-            if len(audio_file) >= 2:
-                # Ensure we have actual bytes, not an integer or other type
-                audio_data = audio_file[1]
-                if not isinstance(audio_data, bytes):
-                    raise ValueError(
-                        f"Expected bytes for audio data, got {type(audio_data)}: {audio_data}"
-                    )
-                audio_bytes = audio_data
-                mimetype = audio_file[2] if len(audio_file) >= 3 else "audio/wav"
-                return (audio_bytes, mimetype)
+        if isinstance(audio_file, (list, tuple)) and len(audio_file) >= 2:
+            audio_data = audio_file[1]
+            if not isinstance(audio_data, bytes):
+                raise ValueError(
+                    f"Expected bytes for audio data, got {type(audio_data)}: {audio_data}"
+                )
+            mimetype = audio_file[2] if len(audio_file) >= 3 else "audio/wav"
+            return (audio_data, mimetype)
 
-        raise ValueError(f"Invalid audio file format in {request.request_type} request")
+        raise ValueError("Invalid audio file format in 'files'.")
 
-    def _extract_prompt(self, request: GenerationRequest) -> str:
+    def _extract_prompt(
+        self,
+        body: dict[str, Any],
+        mode: _Mode,
+    ) -> str:
         """
-        Extract prompt text from generation request.
+        Extract prompt text from request body based on mode.
 
-        For chat completions, uses the tokenizer's chat template if available,
-        otherwise falls back to simple formatting.
+        For chat mode, uses the tokenizer's chat template or plain concat per
+        request_format. For text mode uses body prompt. For audio, prompt is optional.
 
-        :param request: Generation request with body containing prompt data
+        :param body: Request body dict (prompt or messages)
+        :param mode: Inferred mode (audio, chat, text)
         :return: Extracted prompt string
         :raises ValueError: If prompt cannot be extracted
         """
-        body = request.arguments.body or {}
-
-        if request.request_type == "text_completions":
+        if mode == "text":
             prompt = body.get("prompt", "")
             if not prompt:
-                raise ValueError("text_completions request must include 'prompt' in body")
+                raise ValueError("Text request must include 'prompt' in body.")
             return prompt if isinstance(prompt, str) else str(prompt)
 
-        if request.request_type == "chat_completions":
+        if mode == "chat":
             messages = body.get("messages", [])
             if not messages:
                 raise ValueError(
-                    "chat_completions request must include 'messages' in body"
+                    "Chat request must include 'messages' in body."
                 )
 
-            # Try to use tokenizer's chat template if available
-            if self._engine is None:
-                raise RuntimeError("Backend not started up while extracting prompt.")
-            tokenizer = self._engine.tokenizer
-
-            # Convert messages to format expected by chat template
+            # Convert messages to format expected by chat template or plain concat
             formatted_messages = []
             for msg in messages:
                 if isinstance(msg, dict):
                     raw_content = msg.get("content", "")
-                    # Extract text from content (handles both string and list formats)
                     text_content = self._extract_text_from_content(raw_content)
                     formatted_messages.append({
                         "role": msg.get("role", ""),
@@ -438,6 +489,34 @@ def _extract_prompt(self, request: GenerationRequest) -> str:
                 else:
                     formatted_messages.append(msg)
 
+            if self.request_format == "plain":
+                # No chat template: append message content only (e.g. "User: ...\nAssistant: ")
+                parts = []
+                for msg in formatted_messages:
+                    role = msg.get("role", "user")
+                    content = msg.get("content", "")
+                    if role and content:
+                        parts.append(f"{role.capitalize()}: {content}")
+                parts.append("Assistant: ")
+                return "\n".join(parts)
+
+            # default-template, None, or custom template: use tokenizer
+            if self._engine is None:
+                raise RuntimeError("Backend not started up while extracting prompt.")
+            tokenizer = self._engine.tokenizer
+
+            if (
+                self.request_format is not None
+                and self.request_format not in ("plain", "default-template")
+            ):
+                # Custom template: set on tokenizer if not already passed via engine config
+                template_value = self.request_format
+                path = Path(template_value)
+                if path.exists() and path.is_file():
+                    tokenizer.chat_template = path.read_text()
+                else:
+                    tokenizer.chat_template = template_value  # type: ignore[assignment]
+
             prompt = tokenizer.apply_chat_template(  # type: ignore[misc]
                 formatted_messages,
                 tokenize=False,
@@ -445,25 +524,20 @@ def _extract_prompt(self, request: GenerationRequest) -> str:
             )
             if isinstance(prompt, str):
                 return prompt
-            else:
-                raise RuntimeError("Code not setup for state")
+            raise RuntimeError("Code not setup for state")
 
-        if request.request_type in ("audio_transcriptions", "audio_translations"):
-            # For audio requests, prompt is optional (e.g., language hints)
-            body = request.arguments.body or {}
+        if mode == "audio":
             prompt = body.get("prompt", "")
             return prompt if isinstance(prompt, str) else str(prompt) if prompt else ""
+        raise ValueError(f"Unsupported mode: {mode!r}.")
 
-        raise ValueError(f"Unsupported request type: {request.request_type}")
-
-    def _create_sampling_params(self, request: GenerationRequest) -> SamplingParams:
+    def _create_sampling_params(self, body: dict[str, Any]) -> SamplingParams:
         """
-        Create VLLM SamplingParams from generation request.
+        Create VLLM SamplingParams from request body.
 
-        :param request: Generation request with parameters
+        :param body: Request body dict with sampling parameters
         :return: Configured SamplingParams instance
         """
-        body = request.arguments.body or {}
 
         # Extract common parameters
         params = {
@@ -481,16 +555,18 @@ def _create_sampling_params(self, request: GenerationRequest) -> SamplingParams:
         return SamplingParams(**params)  # type: ignore[misc]
 
     def _convert_vllm_output_to_openai_format(
-        self, request: GenerationRequest, output: RequestOutput
+        self,
+        output: RequestOutput,
+        mode: _Mode,
     ) -> dict[str, Any]:
         """
         Convert VLLM RequestOutput to OpenAI-style dict format.
 
         Converts VLLM's native output format to the format expected by
-        GenerationResponseHandler, matching OpenAI API response structure.
+        VLLMResponseHandler, matching OpenAI API response structure.
 
-        :param request: Original generation request
         :param output: VLLM request output
+        :param mode: Inferred mode (audio, chat, text) for response shape
         :return: OpenAI-style response dictionary
         """
         # Extract generated text
@@ -513,24 +589,21 @@ def _convert_vllm_output_to_openai_format(
             "total_tokens": input_tokens + output_tokens,
         }
 
-        # Audio responses use different format: {"text": "...", "usage": {...}}
-        if request.request_type in ("audio_transcriptions", "audio_translations"):
+        if mode == "audio":
             response_dict: dict[str, Any] = {
                 "text": generated_text,
                 "usage": usage,
             }
+        elif mode == "text":
+            response_dict = {
+                "choices": [{"text": generated_text}],
+                "usage": usage,
+            }
         else:
-            # Build choices array based on request type
-            if request.request_type == "text_completions":
-                choices = [{"text": generated_text}]
-            elif request.request_type == "chat_completions":
-                choices = [{"message": {"content": generated_text, "role": "assistant"}}]
-            else:
-                choices = [{"text": generated_text}]
-
-            # Build response dict
             response_dict = {
-                "choices": choices,
+                "choices": [
+                    {"message": {"content": generated_text, "role": "assistant"}}
+                ],
                 "usage": usage,
             }
 
@@ -559,30 +632,27 @@ async def resolve(  # type: ignore[override]
         :param history: Conversation history (currently not supported)
         :raises NotImplementedError: If history is provided
         :raises RuntimeError: If backend is not initialized
-        :raises ValueError: If request type is unsupported
+        :raises ValueError: If request body/files do not imply a valid mode
         :yields: Tuples of (response, updated_request_info) as generation progresses
         """
+        # Resolve request context (supports standard pipeline with columns only)
+        ctx = self._get_request_context(request)
+
         # Validate request
         self._validate_backend_initialized()
         self._validate_history(history)
-        self._validate_request_type(request.request_type)
 
-        # Create response handler
-        response_handler = GenerationResponseHandlerFactory.create(request.request_type)
+        # Create response handler (no request type; handler infers from response shape)
+        response_handler = VLLMResponseHandler()
 
         # Extract prompt and create sampling parameters
-        prompt = self._extract_prompt(request)
-        sampling_params = self._create_sampling_params(request)
-        stream = request.arguments.stream or False
+        prompt = self._extract_prompt(ctx.body, ctx.mode)
+        sampling_params = self._create_sampling_params(ctx.body)
+        stream = ctx.stream
 
         # For audio requests, construct multimodal input with audio data
-        if request.request_type in ("audio_transcriptions", "audio_translations"):
-            audio_data = self._extract_audio_from_request(request)
-            if audio_data is None:
-                raise ValueError(
-                    f"{request.request_type} request must include audio file in 'files'"
-                )
-            audio_bytes, mimetype = audio_data
+        if ctx.mode == "audio":
+            audio_bytes, mimetype = self._extract_audio_from_request(ctx.files)
 
             # Validate audio_bytes is actually bytes
             if not isinstance(audio_bytes, bytes):
@@ -660,7 +730,7 @@ async def resolve(  # type: ignore[override]
                 # Convert to OpenAI format and use handler
                 if final_output is not None:
                     openai_format = self._convert_vllm_output_to_openai_format(
-                        request, final_output
+                        final_output, ctx.mode
                     )
                     yield response_handler.compile_non_streaming(request, openai_format), request_info
 
@@ -668,7 +738,7 @@ async def resolve(  # type: ignore[override]
                 # Yield current result to store iterative results before propagating
                 if final_output is not None:
                     openai_format = self._convert_vllm_output_to_openai_format(
-                        request, final_output
+                        final_output, ctx.mode
                     )
                     yield response_handler.compile_non_streaming(request, openai_format), request_info
                 raise err
@@ -708,13 +778,12 @@ async def resolve(  # type: ignore[override]
                         # Calculate delta (new text)
                         delta_text = generated_text[len(accumulated_text) :]
 
-                        # Build streaming delta format based on request type
-                        if request.request_type == "text_completions":
+                        # Build streaming delta format based on mode
+                        if ctx.mode == "text":
                             delta_data = {"choices": [{"text": delta_text}]}
-                        elif request.request_type in ("audio_transcriptions", "audio_translations"):
-                            # Audio streaming uses {"text": "..."} format
+                        elif ctx.mode == "audio":
                             delta_data = {"text": delta_text}
-                        else:  # chat_completions
+                        else:
                             delta_data = {
                                 "choices": [{"delta": {"content": delta_text, "role": "assistant"}}]
                             }
@@ -758,4 +827,3 @@ async def resolve(  # type: ignore[override]
                     ) from exc
                 # Re-raise with context
                 raise RuntimeError(f"Generation failed: {exc}") from exc
-
diff --git a/src/guidellm/backends/vllm_python/vllm_response.py b/src/guidellm/backends/vllm_python/vllm_response.py
new file mode 100644
index 000000000..2446242b3
--- /dev/null
+++ b/src/guidellm/backends/vllm_python/vllm_response.py
@@ -0,0 +1,216 @@
+"""
+VLLM-specific response handler for compiling OpenAI-style response dicts.
+
+Compiles response dictionaries (choices+usage or text+usage) and streaming
+SSE lines into GenerationResponse. Infers format from response shape; no
+request type parameter required.
+"""
+
+from __future__ import annotations
+
+from typing import Any, cast
+
+from guidellm.schemas import GenerationRequest, GenerationResponse, UsageMetrics
+from guidellm.utils import json
+
+__all__ = ["VLLMResponseHandler"]
+
+
+class VLLMResponseHandler:
+    """
+    Response handler for the vLLM Python backend.
+
+    Compiles OpenAI-style response dicts into GenerationResponse by inferring
+    shape: choices+usage (text/chat) or text+usage (audio). Parses streaming
+    deltas (choices[].text, choices[].delta.content, or text) without
+    requiring a request type.
+    """
+
+    def __init__(self) -> None:
+        """Initialize streaming accumulation state."""
+        self.streaming_texts: list[str] = []
+        self.streaming_usage: dict[str, Any] | None = None
+        self.streaming_response_id: str | None = None
+
+    def compile_non_streaming(
+        self,
+        request: GenerationRequest,
+        response_dict: dict[str, Any],
+    ) -> GenerationResponse:
+        """
+        Compile a non-streaming response dict into GenerationResponse.
+
+        Infers format from keys: "text" (audio-style) vs "choices" (text/chat).
+
+        :param request: Original generation request
+        :param response_dict: OpenAI-style response (choices+usage or text+usage)
+        :return: GenerationResponse with text and metrics
+        """
+        text, usage = self._extract_text_and_usage(response_dict)
+        input_metrics, output_metrics = self._extract_metrics(usage, text)
+        request_args = self._get_request_args(request)
+
+        return GenerationResponse(
+            request_id=request.request_id,
+            response_id=response_dict.get("id"),
+            request_args=request_args,
+            text=text or None,
+            input_metrics=input_metrics,
+            output_metrics=output_metrics,
+        )
+
+    def add_streaming_line(self, line: str) -> int | None:
+        """
+        Process a single SSE line from a streaming response.
+
+        Infers delta shape: "text", choices[].text, or choices[].delta.content.
+        Returns None if line indicates completion (e.g. data: [DONE]).
+
+        :param line: Raw SSE line (e.g. "data: {...}")
+        :return: 1 if content was extracted, 0 if ignored, None if stream done
+        """
+        data = self._parse_line(line)
+        if data is None:
+            return None
+        if not data:
+            return 0
+
+        if "id" in data and self.streaming_response_id is None:
+            self.streaming_response_id = data.get("id")
+
+        if "usage" in data:
+            self.streaming_usage = data.get("usage") or self.streaming_usage
+
+        # Audio-style: {"text": "..."}
+        if "text" in data and data["text"]:
+            self.streaming_texts.append(data["text"])
+            return 1
+
+        # Choices-style
+        choices = data.get("choices", [])
+        if not choices:
+            return 0
+
+        choice = choices[0]
+        # Text completions: {"choices": [{"text": "..."}]}
+        if "text" in choice and choice["text"]:
+            self.streaming_texts.append(choice["text"])
+            return 1
+        # Chat completions: {"choices": [{"delta": {"content": "..."}}]}
+        delta = choice.get("delta", {})
+        if isinstance(delta, dict) and delta.get("content"):
+            self.streaming_texts.append(delta["content"])
+            return 1
+
+        return 0
+
+    def compile_streaming(self, request: GenerationRequest) -> GenerationResponse:
+        """
+        Compile accumulated streaming chunks into a final GenerationResponse.
+
+        :param request: Original generation request
+        :return: GenerationResponse with concatenated text and metrics
+        """
+        text = "".join(self.streaming_texts)
+        input_metrics, output_metrics = self._extract_metrics(self.streaming_usage, text)
+        request_args = self._get_request_args(request)
+
+        return GenerationResponse(
+            request_id=request.request_id,
+            response_id=self.streaming_response_id,
+            request_args=request_args,
+            text=text or None,
+            input_metrics=input_metrics,
+            output_metrics=output_metrics,
+        )
+
+    def _extract_text_and_usage(
+        self, response_dict: dict[str, Any]
+    ) -> tuple[str, dict[str, Any]]:
+        """Extract text and usage from response dict; infer shape from keys."""
+        usage = response_dict.get("usage", {}) or {}
+
+        if "text" in response_dict:
+            text = response_dict["text"]
+            return (text if isinstance(text, str) else str(text), usage)
+
+        choices = response_dict.get("choices", [])
+        if not choices:
+            return ("", usage)
+
+        choice = choices[0]
+        if "text" in choice:
+            text = choice["text"]
+        elif "message" in choice:
+            msg = choice["message"]
+            text = msg.get("content", "") if isinstance(msg, dict) else ""
+        else:
+            text = ""
+
+        return (text if isinstance(text, str) else str(text), usage)
+
+    def _extract_metrics(
+        self, usage: dict[str, Any] | None, text: str
+    ) -> tuple[UsageMetrics, UsageMetrics]:
+        """Build UsageMetrics from usage dict and text (word/char counts)."""
+        if not usage:
+            return UsageMetrics(), UsageMetrics(
+                text_words=len(text.split()) if text else 0,
+                text_characters=len(text) if text else 0,
+            )
+
+        usage_metrics = cast("dict[str, int]", usage)
+        input_details = cast(
+            "dict[str, int]", usage.get("prompt_tokens_details", {}) or {}
+        )
+        output_details = cast(
+            "dict[str, int]", usage.get("completion_tokens_details", {}) or {}
+        )
+
+        input_metrics = UsageMetrics(
+            text_tokens=(
+                input_details.get("prompt_tokens")
+                or usage_metrics.get("prompt_tokens")
+                or 0
+            ),
+            image_tokens=input_details.get("image_tokens"),
+            video_tokens=input_details.get("video_tokens"),
+            audio_tokens=input_details.get("audio_tokens"),
+            audio_seconds=input_details.get("seconds"),
+        )
+        output_metrics = UsageMetrics(
+            text_tokens=(
+                output_details.get("completion_tokens")
+                or usage_metrics.get("completion_tokens")
+                or 0
+            ),
+            text_words=len(text.split()) if text else 0,
+            text_characters=len(text) if text else 0,
+            image_tokens=output_details.get("image_tokens"),
+            video_tokens=output_details.get("video_tokens"),
+            audio_tokens=output_details.get("audio_tokens"),
+            audio_seconds=output_details.get("seconds"),
+        )
+        return input_metrics, output_metrics
+
+    def _parse_line(self, line: str) -> dict[str, Any] | None:
+        """Parse SSE line to JSON dict; return None if done or invalid."""
+        if line == "data: [DONE]":
+            return None
+        line = (line or "").strip()
+        if not line or not line.startswith("data:"):
+            return {} if line else None
+        line = line[len("data:") :].strip()
+        try:
+            return json.loads(line)
+        except Exception:
+            return {}
+
+    def _get_request_args(self, request: GenerationRequest) -> str | None:
+        """Get request_args string from request.arguments if present."""
+        arguments = getattr(request, "arguments", None)
+        if arguments is None:
+            return None
+        if hasattr(arguments, "model_dump_json"):
+            return arguments.model_dump_json()
+        return None
diff --git a/src/guidellm/benchmark/schemas/generative/entrypoints.py b/src/guidellm/benchmark/schemas/generative/entrypoints.py
index 70a97413a..04e240800 100644
--- a/src/guidellm/benchmark/schemas/generative/entrypoints.py
+++ b/src/guidellm/benchmark/schemas/generative/entrypoints.py
@@ -235,8 +235,9 @@ def get_default(cls: type[BenchmarkGenerativeTextArgs], field: str) -> Any:
     request_format: str | None = Field(
         default=None,
         description=(
-            "Query format for backend operations;"
-            " shorthand for backend_kwargs['request_format']"
+            "Query format for backend operations; shorthand for "
+            "backend_kwargs['request_format']. For vLLM backend: plain, "
+            "default-template, or chat template path/single-line string."
         ),
     )
     model: str | None = Field(default=None, description="Model identifier for backend")

From e5aa345818cc5dbded749c0e0b3f25efd38534f5 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Tue, 10 Feb 2026 00:11:08 -0500
Subject: [PATCH 26/56] Default to streaming and fix token counts

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 20 ++++++++++++++++++--
 1 file changed, 18 insertions(+), 2 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 6e930bc1f..939ba6e68 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -330,7 +330,9 @@ def _get_request_context(self, request: GenerationRequest) -> _RequestContext:
                         "content": [{"type": "text", "text": str(t)}],
                     })
             body = {"messages": messages}
-            stream = False
+            # Default to streaming when using standard pipeline (no arguments), to match
+            # OpenAI HTTP backend behavior and enable correct token timing metrics.
+            stream = True
             files = {}
 
         if files:
@@ -809,8 +811,22 @@ async def resolve(  # type: ignore[override]
 
                 request_info.timings.request_end = time.time()
 
+                # Inject real token counts into handler so compile_streaming gets correct metrics
+                if final_output is not None:
+                    input_tokens = len(final_output.prompt_token_ids)
+                    output_tokens = 0
+                    if final_output.outputs and len(final_output.outputs) > 0:
+                        out = final_output.outputs[0]
+                        if out.token_ids is not None:
+                            output_tokens = len(out.token_ids)
+                    response_handler.streaming_usage = {
+                        "prompt_tokens": input_tokens,
+                        "completion_tokens": output_tokens,
+                        "total_tokens": input_tokens + output_tokens,
+                    }
                 # Yield final compiled response
-                yield response_handler.compile_streaming(request), request_info
+                response = response_handler.compile_streaming(request)
+                yield response, request_info
 
             except asyncio.CancelledError as err:
                 # Yield current result to store iterative results before propagating

From 8a35197da7b1d79a7d3feeb99adef8d1b4f2fd87 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Tue, 10 Feb 2026 00:34:20 -0500
Subject: [PATCH 27/56] Attempt to fix token stats for vllm backend

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 69 ++++++++++++++++++-----
 src/guidellm/schemas/request_stats.py     | 13 +++--
 2 files changed, 63 insertions(+), 19 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 939ba6e68..b1c9bfa2d 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -379,6 +379,47 @@ def _update_token_timing(
         request_info.timings.last_token_iteration = iter_time
         request_info.timings.token_iterations += iterations
 
+    def _streaming_usage_from_output(
+        self,
+        final_output: RequestOutput | None,
+        accumulated_text: str,
+        request_info: RequestInfo,
+    ) -> dict[str, int] | None:
+        """
+        Build usage dict (prompt_tokens, completion_tokens, total_tokens) for streaming.
+
+        Uses token_ids when available; otherwise tokenizes generated text with the
+        engine tokenizer, or falls back to token_iterations.
+        """
+        if final_output is None:
+            return None
+        input_tokens = len(final_output.prompt_token_ids)
+        output_tokens = 0
+        generated_text = accumulated_text
+        if final_output.outputs and len(final_output.outputs) > 0:
+            out = final_output.outputs[0]
+            if not generated_text and out.text:
+                generated_text = out.text
+            if out.token_ids is not None:
+                output_tokens = len(out.token_ids)
+        if output_tokens == 0 and generated_text and self._engine is not None:
+            try:
+                tokenizer = getattr(self._engine, "tokenizer", None)
+                if tokenizer is not None:
+                    ids = tokenizer.encode(  # type: ignore[union-attr]
+                        generated_text, add_special_tokens=False
+                    )
+                    output_tokens = len(ids)
+            except Exception:
+                pass
+        if output_tokens == 0 and request_info.timings.token_iterations:
+            output_tokens = request_info.timings.token_iterations
+        return {
+            "prompt_tokens": input_tokens,
+            "completion_tokens": output_tokens,
+            "total_tokens": input_tokens + output_tokens,
+        }
+
     def _extract_text_from_content(self, content: str | list[dict[str, Any]] | Any) -> str:
         """
         Extract text content from message content field.
@@ -812,24 +853,26 @@ async def resolve(  # type: ignore[override]
                 request_info.timings.request_end = time.time()
 
                 # Inject real token counts into handler so compile_streaming gets correct metrics
-                if final_output is not None:
-                    input_tokens = len(final_output.prompt_token_ids)
-                    output_tokens = 0
-                    if final_output.outputs and len(final_output.outputs) > 0:
-                        out = final_output.outputs[0]
-                        if out.token_ids is not None:
-                            output_tokens = len(out.token_ids)
-                    response_handler.streaming_usage = {
-                        "prompt_tokens": input_tokens,
-                        "completion_tokens": output_tokens,
-                        "total_tokens": input_tokens + output_tokens,
-                    }
+                usage = self._streaming_usage_from_output(
+                    final_output, accumulated_text, request_info
+                )
+                if usage is not None:
+                    response_handler.streaming_usage = usage
+                    logger.debug(
+                        "[vllm_python streaming] injected usage completion_tokens={}",
+                        usage.get("completion_tokens"),
+                    )
                 # Yield final compiled response
                 response = response_handler.compile_streaming(request)
                 yield response, request_info
 
             except asyncio.CancelledError as err:
-                # Yield current result to store iterative results before propagating
+                # Yield current result; inject usage from last known state if available
+                usage = self._streaming_usage_from_output(
+                    final_output, accumulated_text, request_info
+                )
+                if usage is not None:
+                    response_handler.streaming_usage = usage
                 yield response_handler.compile_streaming(request), request_info
                 raise err
             except Exception as exc:
diff --git a/src/guidellm/schemas/request_stats.py b/src/guidellm/schemas/request_stats.py
index e94e31c38..822ab7d10 100644
--- a/src/guidellm/schemas/request_stats.py
+++ b/src/guidellm/schemas/request_stats.py
@@ -119,12 +119,13 @@ def output_tokens(self) -> int | None:
         """
         :return: Number of tokens in the generated output, or None if unavailable
         """
-        # Fallback if we did not get usage metrics from the server
-        # NOTE: This assumes each iteration is one token
-        if self.output_metrics.total_tokens is None:
-            return self.info.timings.token_iterations or None
-
-        return self.output_metrics.total_tokens
+        # Prefer total_tokens; fall back to text_tokens (e.g. from injected streaming usage)
+        # then token_iterations (assumes each iteration is one token)
+        if self.output_metrics.total_tokens is not None:
+            return self.output_metrics.total_tokens
+        if self.output_metrics.text_tokens is not None:
+            return self.output_metrics.text_tokens
+        return self.info.timings.token_iterations or None
 
     @computed_field  # type: ignore[misc]
     @property

From ccecff37c529023d102de21b5d9eba3446ab9a09 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Tue, 10 Feb 2026 00:48:12 -0500
Subject: [PATCH 28/56] Remove log message

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index b1c9bfa2d..3f1986a9b 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -858,10 +858,6 @@ async def resolve(  # type: ignore[override]
                 )
                 if usage is not None:
                     response_handler.streaming_usage = usage
-                    logger.debug(
-                        "[vllm_python streaming] injected usage completion_tokens={}",
-                        usage.get("completion_tokens"),
-                    )
                 # Yield final compiled response
                 response = response_handler.compile_streaming(request)
                 yield response, request_info

From dbcb13fa6fb8933401add313be96564a2e9c1296 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Tue, 10 Feb 2026 13:23:42 -0500
Subject: [PATCH 29/56] Improve timing data for request stats

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/openai/http.py      |  4 ++
 src/guidellm/backends/vllm_python/vllm.py | 71 ++++++++++++++++++++---
 src/guidellm/schemas/info.py              |  4 ++
 src/guidellm/schemas/request_stats.py     |  8 +++
 4 files changed, 80 insertions(+), 7 deletions(-)

diff --git a/src/guidellm/backends/openai/http.py b/src/guidellm/backends/openai/http.py
index 86bdb0e3d..b571c22ea 100644
--- a/src/guidellm/backends/openai/http.py
+++ b/src/guidellm/backends/openai/http.py
@@ -358,6 +358,7 @@ async def resolve(  # type: ignore[override]
 
         try:
             request_info.timings.request_start = time.time()
+            request_info.timings.output_token_iteration_timings = []
 
             async with self._async_client.stream(
                 arguments.method or "POST",
@@ -390,6 +391,9 @@ async def resolve(  # type: ignore[override]
 
                     request_info.timings.last_token_iteration = iter_time
                     request_info.timings.token_iterations += iterations
+                    request_info.timings.output_token_iteration_timings.append(
+                        (iter_time, float(iterations))
+                    )
 
             request_info.timings.request_end = time.time()
             yield request_handler.compile_streaming(request, arguments), request_info
diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 3f1986a9b..582cd70a2 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -34,6 +34,7 @@
     RequestOutput = None  # type: ignore[assignment, misc]
     HAS_VLLM = False
 
+from guidellm.logger import logger
 from guidellm.backends.backend import Backend
 from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler
 from guidellm.extras.audio import _decode_audio
@@ -384,24 +385,37 @@ def _streaming_usage_from_output(
         final_output: RequestOutput | None,
         accumulated_text: str,
         request_info: RequestInfo,
+        accumulated_output_tokens: int | None = None,
     ) -> dict[str, int] | None:
         """
         Build usage dict (prompt_tokens, completion_tokens, total_tokens) for streaming.
 
-        Uses token_ids when available; otherwise tokenizes generated text with the
-        engine tokenizer, or falls back to token_iterations.
+        When accumulated_output_tokens is provided (from summing chunk token_ids in the
+        loop), uses that. Otherwise uses token_ids from final_output (last chunk only),
+        then tokenizer, then token_iterations fallback.
         """
         if final_output is None:
             return None
         input_tokens = len(final_output.prompt_token_ids)
         output_tokens = 0
+        source = "unknown"
         generated_text = accumulated_text
-        if final_output.outputs and len(final_output.outputs) > 0:
+        if accumulated_output_tokens is not None and accumulated_output_tokens > 0:
+            output_tokens = accumulated_output_tokens
+            source = "token_ids"
+        elif final_output.outputs and len(final_output.outputs) > 0:
             out = final_output.outputs[0]
             if not generated_text and out.text:
                 generated_text = out.text
             if out.token_ids is not None:
                 output_tokens = len(out.token_ids)
+                source = "token_ids"
+            else:
+                logger.debug(
+                    "[vllm_python streaming] final_output.outputs[0].token_ids is None, "
+                    "len(generated_text)={}",
+                    len(generated_text or ""),
+                )
         if output_tokens == 0 and generated_text and self._engine is not None:
             try:
                 tokenizer = getattr(self._engine, "tokenizer", None)
@@ -410,10 +424,27 @@ def _streaming_usage_from_output(
                         generated_text, add_special_tokens=False
                     )
                     output_tokens = len(ids)
-            except Exception:
-                pass
+                    source = "tokenizer"
+                else:
+                    logger.debug(
+                        "[vllm_python streaming] engine has no tokenizer attribute"
+                    )
+            except Exception as exc:
+                logger.debug(
+                    "[vllm_python streaming] tokenizer.encode failed: {}",
+                    exc,
+                )
         if output_tokens == 0 and request_info.timings.token_iterations:
             output_tokens = request_info.timings.token_iterations
+            source = "token_iterations"
+        logger.warning(
+            "[vllm_python streaming usage] source={} completion_tokens={} "
+            "prompt_tokens={} token_iterations={}",
+            source,
+            output_tokens,
+            input_tokens,
+            request_info.timings.token_iterations,
+        )
         return {
             "prompt_tokens": input_tokens,
             "completion_tokens": output_tokens,
@@ -801,8 +832,10 @@ async def resolve(  # type: ignore[override]
             # Streaming path
             try:
                 request_info.timings.request_start = time.time()
+                request_info.timings.output_token_iteration_timings = []
                 accumulated_text = ""
                 final_output: RequestOutput | None = None
+                total_output_tokens = 0
 
                 # Use AsyncLLMEngine's native async generation
                 async for request_output in self._engine.generate(  # type: ignore[misc]
@@ -811,6 +844,13 @@ async def resolve(  # type: ignore[override]
                     iter_time = time.time()
                     self._update_request_timing(request_info, iter_time)
 
+                    # Accumulate output token count from each chunk (vLLM streaming gives
+                    # token_ids per chunk, not cumulative)
+                    if request_output.outputs and len(request_output.outputs) > 0:
+                        out = request_output.outputs[0]
+                        if out.token_ids is not None:
+                            total_output_tokens += len(out.token_ids)
+
                     # Extract generated text from output
                     generated_text = ""
                     if request_output.outputs and len(request_output.outputs) > 0:
@@ -844,6 +884,17 @@ async def resolve(  # type: ignore[override]
                         iterations = response_handler.add_streaming_line(sse_line)
 
                         if iterations is not None and iterations > 0:
+                            chunk_token_count = 1
+                            if request_output.outputs and len(request_output.outputs) > 0:
+                                out = request_output.outputs[0]
+                                chunk_token_count = (
+                                    len(out.token_ids)
+                                    if out.token_ids is not None
+                                    else 1
+                                )
+                            request_info.timings.output_token_iteration_timings.append(
+                                (iter_time, float(chunk_token_count))
+                            )
                             self._update_token_timing(request_info, iter_time, iterations)
 
                         accumulated_text = generated_text
@@ -854,7 +905,10 @@ async def resolve(  # type: ignore[override]
 
                 # Inject real token counts into handler so compile_streaming gets correct metrics
                 usage = self._streaming_usage_from_output(
-                    final_output, accumulated_text, request_info
+                    final_output,
+                    accumulated_text,
+                    request_info,
+                    accumulated_output_tokens=total_output_tokens,
                 )
                 if usage is not None:
                     response_handler.streaming_usage = usage
@@ -865,7 +919,10 @@ async def resolve(  # type: ignore[override]
             except asyncio.CancelledError as err:
                 # Yield current result; inject usage from last known state if available
                 usage = self._streaming_usage_from_output(
-                    final_output, accumulated_text, request_info
+                    final_output,
+                    accumulated_text,
+                    request_info,
+                    accumulated_output_tokens=total_output_tokens,
                 )
                 if usage is not None:
                     response_handler.streaming_usage = usage
diff --git a/src/guidellm/schemas/info.py b/src/guidellm/schemas/info.py
index e5481b276..bb60db128 100644
--- a/src/guidellm/schemas/info.py
+++ b/src/guidellm/schemas/info.py
@@ -71,6 +71,10 @@ class RequestTimings(StandardBaseDict):
     token_iterations: int = Field(
         default=0,
     )
+    output_token_iteration_timings: list[tuple[float, float]] | None = Field(
+        default=None,
+        description="Chronological (timestamp, token_count) per stream chunk when available",
+    )
     request_end: float | None = Field(
         default=None,
         description="Unix timestamp when the backend completed processing the request",
diff --git a/src/guidellm/schemas/request_stats.py b/src/guidellm/schemas/request_stats.py
index 822ab7d10..8e5016a6b 100644
--- a/src/guidellm/schemas/request_stats.py
+++ b/src/guidellm/schemas/request_stats.py
@@ -289,6 +289,10 @@ def output_tokens_timings(self) -> list[tuple[float, float]]:
         :return: List of (timestamp, token_count) tuples for output token generations
         :raises ValueError: If resolve_end timings are not set
         """
+        real = self.info.timings.output_token_iteration_timings
+        if real is not None and len(real) > 0:
+            return [(float(t), float(n)) for t, n in real]
+
         if (
             self.first_token_iteration is None
             or self.last_token_iteration is None
@@ -317,6 +321,10 @@ def iter_tokens_timings(self) -> list[tuple[float, float]]:
         :return: List of (timestamp, token_count) tuples for iterations excluding
             first token
         """
+        real = self.info.timings.output_token_iteration_timings
+        if real is not None and len(real) > 1:
+            return [(float(t), float(n)) for t, n in real[1:]]
+
         if (
             self.first_token_iteration is None
             or self.last_token_iteration is None

From f6e277c2cf7e8e838f22136191babaf297e9e3a4 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Tue, 10 Feb 2026 23:49:38 -0500
Subject: [PATCH 30/56] Fix counting of tokens

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 582cd70a2..98dd41086 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -844,13 +844,6 @@ async def resolve(  # type: ignore[override]
                     iter_time = time.time()
                     self._update_request_timing(request_info, iter_time)
 
-                    # Accumulate output token count from each chunk (vLLM streaming gives
-                    # token_ids per chunk, not cumulative)
-                    if request_output.outputs and len(request_output.outputs) > 0:
-                        out = request_output.outputs[0]
-                        if out.token_ids is not None:
-                            total_output_tokens += len(out.token_ids)
-
                     # Extract generated text from output
                     generated_text = ""
                     if request_output.outputs and len(request_output.outputs) > 0:
@@ -892,6 +885,7 @@ async def resolve(  # type: ignore[override]
                                     if out.token_ids is not None
                                     else 1
                                 )
+                            total_output_tokens += chunk_token_count
                             request_info.timings.output_token_iteration_timings.append(
                                 (iter_time, float(chunk_token_count))
                             )

From f7d908cf6f5f55637107dc0b6b0f401aa4277d5e Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 00:24:40 -0500
Subject: [PATCH 31/56] Fix max token count logic

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 23 ++++++++++++++++++++---
 1 file changed, 20 insertions(+), 3 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 98dd41086..588b0f116 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -605,19 +605,29 @@ def _extract_prompt(
             return prompt if isinstance(prompt, str) else str(prompt) if prompt else ""
         raise ValueError(f"Unsupported mode: {mode!r}.")
 
-    def _create_sampling_params(self, body: dict[str, Any]) -> SamplingParams:
+    def _create_sampling_params(
+        self,
+        body: dict[str, Any],
+        max_tokens_override: int | None = None,
+    ) -> SamplingParams:
         """
         Create VLLM SamplingParams from request body.
 
         :param body: Request body dict with sampling parameters
+        :param max_tokens_override: Optional max_tokens from request (e.g. benchmark target)
         :return: Configured SamplingParams instance
         """
+        max_tokens = (
+            body.get("max_tokens")
+            if body.get("max_tokens") is not None
+            else (max_tokens_override if max_tokens_override is not None else 16)
+        )
 
         # Extract common parameters
         params = {
             "temperature": body.get("temperature", 1.0),
             "top_p": body.get("top_p", 1.0),
-            "max_tokens": body.get("max_tokens", 16),
+            "max_tokens": max_tokens,
             "stop": body.get("stop", None),
             "frequency_penalty": body.get("frequency_penalty", 0.0),
             "presence_penalty": body.get("presence_penalty", 0.0),
@@ -721,7 +731,14 @@ async def resolve(  # type: ignore[override]
 
         # Extract prompt and create sampling parameters
         prompt = self._extract_prompt(ctx.body, ctx.mode)
-        sampling_params = self._create_sampling_params(ctx.body)
+        sampling_params = self._create_sampling_params(
+            ctx.body,
+            max_tokens_override=(
+                request.output_metrics.text_tokens
+                if request.output_metrics.text_tokens
+                else None
+            ),
+        )
         stream = ctx.stream
 
         # For audio requests, construct multimodal input with audio data

From d8aa3597ded7a50e5297e358ddbd3b495626d06e Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 00:31:43 -0500
Subject: [PATCH 32/56] Help vllm python behavior match http backend

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 588b0f116..bd0ce3a69 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -623,17 +623,24 @@ def _create_sampling_params(
             else (max_tokens_override if max_tokens_override is not None else 16)
         )
 
+        # When using benchmark target (max_tokens_override), ignore EOS and stop so
+        # generation continues until max_tokens (match HTTP backend behavior).
+        using_override = (
+            max_tokens_override is not None and body.get("max_tokens") is None
+        )
+
         # Extract common parameters
         params = {
             "temperature": body.get("temperature", 1.0),
             "top_p": body.get("top_p", 1.0),
             "max_tokens": max_tokens,
-            "stop": body.get("stop", None),
+            "stop": None if using_override else body.get("stop", None),
+            "ignore_eos": True if using_override else body.get("ignore_eos", False),
             "frequency_penalty": body.get("frequency_penalty", 0.0),
             "presence_penalty": body.get("presence_penalty", 0.0),
         }
 
-        # Remove None values
+        # Remove None values (except ignore_eos which is bool)
         params = {k: v for k, v in params.items() if v is not None}
 
         return SamplingParams(**params)  # type: ignore[misc]

From f62abc9a5898b0f36a31da4778a83ced112bcd9b Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 00:38:15 -0500
Subject: [PATCH 33/56] Help vllm python behavior match http backend

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 20 ++++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index bd0ce3a69..81f7de271 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -623,19 +623,31 @@ def _create_sampling_params(
             else (max_tokens_override if max_tokens_override is not None else 16)
         )
 
-        # When using benchmark target (max_tokens_override), ignore EOS and stop so
-        # generation continues until max_tokens (match HTTP backend behavior).
+        # When generating to benchmark target (max_tokens from override or body equals
+        # override), ignore EOS and stop so generation runs until max_tokens.
         using_override = (
             max_tokens_override is not None and body.get("max_tokens") is None
         )
+        generating_to_benchmark_target = (
+            max_tokens_override is not None and max_tokens == max_tokens_override
+        )
+        ignore_eos = (
+            generating_to_benchmark_target
+            or body.get("ignore_eos", False)
+        )
+        if generating_to_benchmark_target and ignore_eos:
+            logger.info(
+                "[vllm_python] Using benchmark target max_tokens=%s with ignore_eos=True",
+                max_tokens,
+            )
 
         # Extract common parameters
         params = {
             "temperature": body.get("temperature", 1.0),
             "top_p": body.get("top_p", 1.0),
             "max_tokens": max_tokens,
-            "stop": None if using_override else body.get("stop", None),
-            "ignore_eos": True if using_override else body.get("ignore_eos", False),
+            "stop": [] if generating_to_benchmark_target else body.get("stop", None),
+            "ignore_eos": ignore_eos,
             "frequency_penalty": body.get("frequency_penalty", 0.0),
             "presence_penalty": body.get("presence_penalty", 0.0),
         }

From 14b049c7381b237513c95d9702434d1fcc71b191 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 00:58:25 -0500
Subject: [PATCH 34/56] Help vllm python behavior match http backend

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 81f7de271..39e324a11 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -635,10 +635,16 @@ def _create_sampling_params(
             generating_to_benchmark_target
             or body.get("ignore_eos", False)
         )
+        # min_tokens: require this many tokens before EOS/stop can stop generation.
+        # When ignore_eos is not respected by the engine, this still forces length.
+        min_tokens = (
+            max_tokens if generating_to_benchmark_target else body.get("min_tokens", 0)
+        )
         if generating_to_benchmark_target and ignore_eos:
             logger.info(
-                "[vllm_python] Using benchmark target max_tokens=%s with ignore_eos=True",
+                "[vllm_python] Using benchmark target max_tokens={} with ignore_eos=True, min_tokens={}",
                 max_tokens,
+                min_tokens,
             )
 
         # Extract common parameters
@@ -646,6 +652,7 @@ def _create_sampling_params(
             "temperature": body.get("temperature", 1.0),
             "top_p": body.get("top_p", 1.0),
             "max_tokens": max_tokens,
+            "min_tokens": min_tokens,
             "stop": [] if generating_to_benchmark_target else body.get("stop", None),
             "ignore_eos": ignore_eos,
             "frequency_penalty": body.get("frequency_penalty", 0.0),

From d4ad846c61eef236b172e537737b24089075e5bc Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 14:05:44 -0500
Subject: [PATCH 35/56] Revert "Help vllm python behavior match http backend"

This reverts commit 14b049c7381b237513c95d9702434d1fcc71b191.
---
 src/guidellm/backends/vllm_python/vllm.py | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 39e324a11..81f7de271 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -635,16 +635,10 @@ def _create_sampling_params(
             generating_to_benchmark_target
             or body.get("ignore_eos", False)
         )
-        # min_tokens: require this many tokens before EOS/stop can stop generation.
-        # When ignore_eos is not respected by the engine, this still forces length.
-        min_tokens = (
-            max_tokens if generating_to_benchmark_target else body.get("min_tokens", 0)
-        )
         if generating_to_benchmark_target and ignore_eos:
             logger.info(
-                "[vllm_python] Using benchmark target max_tokens={} with ignore_eos=True, min_tokens={}",
+                "[vllm_python] Using benchmark target max_tokens=%s with ignore_eos=True",
                 max_tokens,
-                min_tokens,
             )
 
         # Extract common parameters
@@ -652,7 +646,6 @@ def _create_sampling_params(
             "temperature": body.get("temperature", 1.0),
             "top_p": body.get("top_p", 1.0),
             "max_tokens": max_tokens,
-            "min_tokens": min_tokens,
             "stop": [] if generating_to_benchmark_target else body.get("stop", None),
             "ignore_eos": ignore_eos,
             "frequency_penalty": body.get("frequency_penalty", 0.0),

From c21e21daf0df2136ae9c65bbb94cbca6d626d9ab Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 14:21:21 -0500
Subject: [PATCH 36/56] Work towards getting vllm-python backend working

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 27 +++++------------------
 1 file changed, 5 insertions(+), 22 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 81f7de271..b6b861243 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -622,37 +622,20 @@ def _create_sampling_params(
             if body.get("max_tokens") is not None
             else (max_tokens_override if max_tokens_override is not None else 16)
         )
+        if max_tokens == 0:
+            max_tokens = 16
 
-        # When generating to benchmark target (max_tokens from override or body equals
-        # override), ignore EOS and stop so generation runs until max_tokens.
-        using_override = (
-            max_tokens_override is not None and body.get("max_tokens") is None
-        )
-        generating_to_benchmark_target = (
-            max_tokens_override is not None and max_tokens == max_tokens_override
-        )
-        ignore_eos = (
-            generating_to_benchmark_target
-            or body.get("ignore_eos", False)
-        )
-        if generating_to_benchmark_target and ignore_eos:
-            logger.info(
-                "[vllm_python] Using benchmark target max_tokens=%s with ignore_eos=True",
-                max_tokens,
-            )
-
-        # Extract common parameters
         params = {
             "temperature": body.get("temperature", 1.0),
             "top_p": body.get("top_p", 1.0),
             "max_tokens": max_tokens,
-            "stop": [] if generating_to_benchmark_target else body.get("stop", None),
-            "ignore_eos": ignore_eos,
+            "min_tokens": body.get("min_tokens", 0),
+            "stop": body.get("stop", None),
+            "ignore_eos": body.get("ignore_eos", False),
             "frequency_penalty": body.get("frequency_penalty", 0.0),
             "presence_penalty": body.get("presence_penalty", 0.0),
         }
 
-        # Remove None values (except ignore_eos which is bool)
         params = {k: v for k, v in params.items() if v is not None}
 
         return SamplingParams(**params)  # type: ignore[misc]

From 8a3e43e4453ae7eb27f1808ccd1fe0c5c7d842a4 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 15:06:28 -0500
Subject: [PATCH 37/56] Try to have vllm backend match http

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index b6b861243..0f1a28998 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -625,13 +625,23 @@ def _create_sampling_params(
         if max_tokens == 0:
             max_tokens = 16
 
+        using_benchmark_target = (
+            body.get("max_tokens") is None and max_tokens_override is not None
+        )
+        if using_benchmark_target:
+            ignore_eos = True
+            stop: list[str] | None = []
+        else:
+            ignore_eos = body.get("ignore_eos", False)
+            stop = body.get("stop", None)
+
         params = {
             "temperature": body.get("temperature", 1.0),
             "top_p": body.get("top_p", 1.0),
             "max_tokens": max_tokens,
             "min_tokens": body.get("min_tokens", 0),
-            "stop": body.get("stop", None),
-            "ignore_eos": body.get("ignore_eos", False),
+            "stop": stop,
+            "ignore_eos": ignore_eos,
             "frequency_penalty": body.get("frequency_penalty", 0.0),
             "presence_penalty": body.get("presence_penalty", 0.0),
         }

From 6493e8e44749ad672f87ed1abf8aac9026a431f8 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 16:46:45 -0500
Subject: [PATCH 38/56] Fix vllm python token counting

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 33 ++++++++++++++---------
 1 file changed, 21 insertions(+), 12 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 0f1a28998..e57eff03a 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -812,7 +812,7 @@ async def resolve(  # type: ignore[override]
             final_output: RequestOutput | None = None
 
             try:
-                # Use AsyncLLMEngine's native async generation
+                # Use AsyncLLMEngine's native async generatio
                 async for request_output in self._engine.generate(  # type: ignore[misc]
                     prompt, sampling_params, request_id
                 ):
@@ -865,6 +865,7 @@ async def resolve(  # type: ignore[override]
                 accumulated_text = ""
                 final_output: RequestOutput | None = None
                 total_output_tokens = 0
+                previous_token_count = 0  # vLLM yields CUMULATIVE token_ids per chunk
 
                 # Use AsyncLLMEngine's native async generation
                 async for request_output in self._engine.generate(  # type: ignore[misc]
@@ -906,18 +907,26 @@ async def resolve(  # type: ignore[override]
                         iterations = response_handler.add_streaming_line(sse_line)
 
                         if iterations is not None and iterations > 0:
-                            chunk_token_count = 1
-                            if request_output.outputs and len(request_output.outputs) > 0:
-                                out = request_output.outputs[0]
-                                chunk_token_count = (
-                                    len(out.token_ids)
-                                    if out.token_ids is not None
-                                    else 1
-                                )
-                            total_output_tokens += chunk_token_count
-                            request_info.timings.output_token_iteration_timings.append(
-                                (iter_time, float(chunk_token_count))
+                            # vLLM token_ids is CUMULATIVE (all tokens so far); add only delta.
+                            out = (
+                                request_output.outputs[0]
+                                if request_output.outputs
+                                else None
                             )
+                            if out is not None and out.token_ids is not None:
+                                current_count = len(out.token_ids)
+                                chunk_token_count = max(
+                                    0, current_count - previous_token_count
+                                )
+                                previous_token_count = current_count
+                            else:
+                                chunk_token_count = 1
+                                previous_token_count += 1
+                            if chunk_token_count > 0:
+                                total_output_tokens += chunk_token_count
+                                request_info.timings.output_token_iteration_timings.append(
+                                    (iter_time, float(chunk_token_count))
+                                )
                             self._update_token_timing(request_info, iter_time, iterations)
 
                         accumulated_text = generated_text

From f2b053358fdeb70a974c2c4a0c08a3f8b877dcb1 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 19:14:11 -0500
Subject: [PATCH 39/56] Simplify stream vs non-stream mode

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 217 ++++++++++------------
 1 file changed, 99 insertions(+), 118 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index e57eff03a..5963d2f64 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -132,6 +132,7 @@ def __init__(
         vllm_config: dict[str, Any] | None = None,
         request_format: str | None = None,
         target: str | None = None,  # Ignored for VLLM Python backend
+        stream: bool = True,
     ):
         """
         Initialize VLLM Python backend with model and configuration.
@@ -145,16 +146,18 @@ def __init__(
             - Any other parameter accepted by vllm.AsyncEngineArgs
             Note: VLLM automatically uses CUDA if available, CPU otherwise.
             The 'device' parameter is not supported and will be ignored.
+        :param request_format: "plain" (no chat template), "default-template" (use
+            tokenizer default), or a chat template path / single-line string.
         :param target: Target URL (ignored for VLLM Python backend, which runs locally)
-        :param kwargs: Additional arguments from the benchmark. request_format can be
-            "plain" (no chat template, text appending only), "default-template" (use
-            tokenizer default), or a vLLM chat template file path / single-line string.
+        :param stream: Whether to stream responses (default True). Can be overridden per
+            request via request.arguments.stream.
         """
         _check_vllm_available()
         super().__init__(type_="vllm_python")
 
         self.model = model
         self.request_format = request_format
+        self.stream = stream
         self.vllm_config = self._merge_config(vllm_config or {})
 
         # Runtime state
@@ -205,6 +208,7 @@ def info(self) -> dict[str, Any]:
         return {
             "model": self.model,
             "vllm_config": self.vllm_config,
+            "stream": self.stream,
             "in_process": self._in_process,
             "engine_initialized": self._engine is not None,
         }
@@ -314,7 +318,8 @@ def _get_request_context(self, request: GenerationRequest) -> _RequestContext:
 
         if arguments is not None:
             body = getattr(arguments, "body", None) or {}
-            stream = bool(getattr(arguments, "stream", False))
+            stream_override = getattr(arguments, "stream", None)
+            stream = self.stream if stream_override is None else bool(stream_override)
             files = getattr(arguments, "files", None) or {}
         else:
             columns = getattr(request, "columns", {}) or {}
@@ -331,9 +336,7 @@ def _get_request_context(self, request: GenerationRequest) -> _RequestContext:
                         "content": [{"type": "text", "text": str(t)}],
                     })
             body = {"messages": messages}
-            # Default to streaming when using standard pipeline (no arguments), to match
-            # OpenAI HTTP backend behavior and enable correct token timing metrics.
-            stream = True
+            stream = self.stream
             files = {}
 
         if files:
@@ -451,6 +454,35 @@ def _streaming_usage_from_output(
             "total_tokens": input_tokens + output_tokens,
         }
 
+    def _build_final_response(
+        self,
+        request: GenerationRequest,
+        request_info: RequestInfo,
+        final_output: RequestOutput | None,
+        ctx: _RequestContext,
+        response_handler: VLLMResponseHandler,
+        stream: bool,
+        accumulated_text: str = "",
+        total_output_tokens: int = 0,
+    ) -> tuple[GenerationResponse, RequestInfo] | None:
+        """Build and return the final (response, request_info) to yield, or None."""
+        if final_output is None:
+            return None
+        if stream:
+            usage = self._streaming_usage_from_output(
+                final_output,
+                accumulated_text,
+                request_info,
+                accumulated_output_tokens=total_output_tokens,
+            )
+            if usage is not None:
+                response_handler.streaming_usage = usage
+            return response_handler.compile_streaming(request), request_info
+        openai_format = self._convert_vllm_output_to_openai_format(
+            final_output, ctx.mode
+        )
+        return response_handler.compile_non_streaming(request, openai_format), request_info
+
     def _extract_text_from_content(self, content: str | list[dict[str, Any]] | Any) -> str:
         """
         Extract text content from message content field.
@@ -716,12 +748,13 @@ async def resolve(  # type: ignore[override]
         history: list[tuple[GenerationRequest, GenerationResponse]] | None = None,
     ) -> AsyncIterator[tuple[GenerationResponse, RequestInfo]]:
         """
-        Process generation request and yield progressive responses.
+        Process generation request and yield a single response.
 
         Handles both streaming and non-streaming requests using AsyncLLMEngine's
-        native async streaming capabilities. For streaming requests, yields
-        token-by-token as they are generated. For non-streaming, yields once
-        with the complete response.
+        native async generation. For streaming, records per-token timings and
+        yields once with a response compiled from the stream. For non-streaming,
+        yields once with the complete response. In both cases the caller receives
+        exactly one (response, request_info) pair.
 
         :param request: Generation request with content and parameters
         :param request_info: Request tracking info updated with timing metadata
@@ -729,7 +762,7 @@ async def resolve(  # type: ignore[override]
         :raises NotImplementedError: If history is provided
         :raises RuntimeError: If backend is not initialized
         :raises ValueError: If request body/files do not imply a valid mode
-        :yields: Tuples of (response, updated_request_info) as generation progresses
+        :yields: Single tuple of (response, updated_request_info)
         """
         # Resolve request context (supports standard pipeline with columns only)
         ctx = self._get_request_context(request)
@@ -806,85 +839,37 @@ async def resolve(  # type: ignore[override]
         # Generate unique request ID for this generation
         request_id = str(uuid.uuid4())
 
-        if not stream:
-            # Non-streaming path
-            request_info.timings.request_start = time.time()
-            final_output: RequestOutput | None = None
-
-            try:
-                # Use AsyncLLMEngine's native async generatio
-                async for request_output in self._engine.generate(  # type: ignore[misc]
-                    prompt, sampling_params, request_id
-                ):
-                    iter_time = time.time()
-                    self._update_request_timing(request_info, iter_time)
-                    final_output = request_output
-
-                    # Check if generation is finished
-                    if (
-                        request_output.outputs
-                        and len(request_output.outputs) > 0
-                        and request_output.outputs[0].finish_reason is not None
-                    ):
-                        break
-
-                request_info.timings.request_end = time.time()
-
-                # Convert to OpenAI format and use handler
-                if final_output is not None:
-                    openai_format = self._convert_vllm_output_to_openai_format(
-                        final_output, ctx.mode
-                    )
-                    yield response_handler.compile_non_streaming(request, openai_format), request_info
+        request_info.timings.request_start = time.time()
+        final_output: RequestOutput | None = None
+        accumulated_text = ""
+        total_output_tokens = 0
+        previous_token_count = 0
+        if stream:
+            request_info.timings.output_token_iteration_timings = []
 
-            except asyncio.CancelledError as err:
-                # Yield current result to store iterative results before propagating
-                if final_output is not None:
-                    openai_format = self._convert_vllm_output_to_openai_format(
-                        final_output, ctx.mode
-                    )
-                    yield response_handler.compile_non_streaming(request, openai_format), request_info
-                raise err
-            except Exception as exc:
-                # Provide clearer error message for audio-related issues
-                error_msg = str(exc)
-                if "At most 0 audio" in error_msg or "audio(s) may be provided" in error_msg:
-                    raise RuntimeError(
-                        f"Generation failed: The model '{self.model}' does not support audio inputs. "
-                        f"For audio transcriptions, please use an audio-capable model (e.g., Whisper-based models). "
-                        f"Original error: {exc}"
-                    ) from exc
-                # Re-raise with context
-                raise RuntimeError(f"Generation failed: {exc}") from exc
-
-        else:
-            # Streaming path
-            try:
-                request_info.timings.request_start = time.time()
-                request_info.timings.output_token_iteration_timings = []
-                accumulated_text = ""
-                final_output: RequestOutput | None = None
-                total_output_tokens = 0
-                previous_token_count = 0  # vLLM yields CUMULATIVE token_ids per chunk
-
-                # Use AsyncLLMEngine's native async generation
-                async for request_output in self._engine.generate(  # type: ignore[misc]
-                    prompt, sampling_params, request_id
+        try:
+            async for request_output in self._engine.generate(  # type: ignore[misc]
+                prompt, sampling_params, request_id
+            ):
+                iter_time = time.time()
+                self._update_request_timing(request_info, iter_time)
+                final_output = request_output
+
+                if (
+                    request_output.outputs
+                    and len(request_output.outputs) > 0
+                    and request_output.outputs[0].finish_reason is not None
                 ):
-                    iter_time = time.time()
-                    self._update_request_timing(request_info, iter_time)
+                    break
 
-                    # Extract generated text from output
+                if stream:
                     generated_text = ""
                     if request_output.outputs and len(request_output.outputs) > 0:
                         generated_text = request_output.outputs[0].text
 
-                    # Check if this is a new token (text has changed)
                     if generated_text != accumulated_text:
-                        # Calculate delta (new text)
                         delta_text = generated_text[len(accumulated_text) :]
 
-                        # Build streaming delta format based on mode
                         if ctx.mode == "text":
                             delta_data = {"choices": [{"text": delta_text}]}
                         elif ctx.mode == "audio":
@@ -894,12 +879,9 @@ async def resolve(  # type: ignore[override]
                                 "choices": [{"delta": {"content": delta_text, "role": "assistant"}}]
                             }
 
-                        # Add response ID if available
                         if hasattr(request_output, "request_id") and request_output.request_id:
                             delta_data["id"] = request_output.request_id
 
-                        # Format as SSE line and feed to handler
-                        # json.dumps may return bytes (orjson) or str, ensure it's a string
                         json_str = json.dumps(delta_data)
                         if isinstance(json_str, bytes):
                             json_str = json_str.decode("utf-8")
@@ -907,7 +889,6 @@ async def resolve(  # type: ignore[override]
                         iterations = response_handler.add_streaming_line(sse_line)
 
                         if iterations is not None and iterations > 0:
-                            # vLLM token_ids is CUMULATIVE (all tokens so far); add only delta.
                             out = (
                                 request_output.outputs[0]
                                 if request_output.outputs
@@ -931,43 +912,43 @@ async def resolve(  # type: ignore[override]
 
                         accumulated_text = generated_text
 
-                    final_output = request_output
-
-                request_info.timings.request_end = time.time()
+            request_info.timings.request_end = time.time()
 
-                # Inject real token counts into handler so compile_streaming gets correct metrics
-                usage = self._streaming_usage_from_output(
+            if (
+                result := self._build_final_response(
+                    request,
+                    request_info,
                     final_output,
+                    ctx,
+                    response_handler,
+                    stream,
                     accumulated_text,
-                    request_info,
-                    accumulated_output_tokens=total_output_tokens,
+                    total_output_tokens,
                 )
-                if usage is not None:
-                    response_handler.streaming_usage = usage
-                # Yield final compiled response
-                response = response_handler.compile_streaming(request)
-                yield response, request_info
-
-            except asyncio.CancelledError as err:
-                # Yield current result; inject usage from last known state if available
-                usage = self._streaming_usage_from_output(
+            ) is not None:
+                yield result
+
+        except asyncio.CancelledError as err:
+            if (
+                result := self._build_final_response(
+                    request,
+                    request_info,
                     final_output,
+                    ctx,
+                    response_handler,
+                    stream,
                     accumulated_text,
-                    request_info,
-                    accumulated_output_tokens=total_output_tokens,
+                    total_output_tokens,
                 )
-                if usage is not None:
-                    response_handler.streaming_usage = usage
-                yield response_handler.compile_streaming(request), request_info
-                raise err
-            except Exception as exc:
-                # Provide clearer error message for audio-related issues
-                error_msg = str(exc)
-                if "At most 0 audio" in error_msg or "audio(s) may be provided" in error_msg:
-                    raise RuntimeError(
-                        f"Generation failed: The model '{self.model}' does not support audio inputs. "
-                        f"For audio transcriptions, please use an audio-capable model (e.g., Whisper-based models). "
-                        f"Original error: {exc}"
-                    ) from exc
-                # Re-raise with context
-                raise RuntimeError(f"Generation failed: {exc}") from exc
+            ) is not None:
+                yield result
+            raise err
+        except Exception as exc:
+            error_msg = str(exc)
+            if "At most 0 audio" in error_msg or "audio(s) may be provided" in error_msg:
+                raise RuntimeError(
+                    f"Generation failed: The model '{self.model}' does not support audio inputs. "
+                    f"For audio transcriptions, please use an audio-capable model (e.g., Whisper-based models). "
+                    f"Original error: {exc}"
+                ) from exc
+            raise RuntimeError(f"Generation failed: {exc}") from exc

From c123b77ddb47cfad02b74b2b8844b8817c23a3d7 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 22:04:36 -0500
Subject: [PATCH 40/56] Improve error validation and messages

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 28 ++++++++++++++++++-----
 1 file changed, 22 insertions(+), 6 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 5963d2f64..60c9914e4 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -78,8 +78,8 @@ class VLLMPythonBackend(Backend):
 
     Directly uses VLLM's AsyncLLMEngine for local async inference. VLLM automatically
     uses CUDA if available, otherwise falls back to CPU. Handles request/response
-    conversion between GuideLLM schemas and VLLM's native API, with true async
-    streaming support for token-by-token generation.
+    conversion between GuideLLM schemas and VLLM's native API, with async support for
+    finer token-by-token processing and timings.
 
     Example:
     ::
@@ -225,8 +225,6 @@ async def process_startup(self):
         if self._in_process:
             raise RuntimeError("Backend already started up for process.")
 
-        # Initialize VLLM AsyncLLMEngine instance
-        # AsyncLLMEngine initialization is async-friendly
         engine_args = AsyncEngineArgs(**self.vllm_config)  # type: ignore[misc]
         self._engine = AsyncLLMEngine.from_engine_args(engine_args)  # type: ignore[misc]
         self._in_process = True
@@ -440,7 +438,7 @@ def _streaming_usage_from_output(
         if output_tokens == 0 and request_info.timings.token_iterations:
             output_tokens = request_info.timings.token_iterations
             source = "token_iterations"
-        logger.warning(
+        logger.debug(
             "[vllm_python streaming usage] source={} completion_tokens={} "
             "prompt_tokens={} token_iterations={}",
             source,
@@ -570,6 +568,15 @@ def _extract_prompt(
         :raises ValueError: If prompt cannot be extracted
         """
         if mode == "text":
+            if (
+                self._engine is not None
+                and getattr(self._engine, "tokenizer", None) is not None
+            ):
+                logger.warning(
+                    "Tokenizer is set (from model) but not used: request has 'prompt' "
+                    "so mode was inferred as text; prompt is passed through without "
+                    "tokenizer formatting."
+                )
             prompt = body.get("prompt", "")
             if not prompt:
                 raise ValueError("Text request must include 'prompt' in body.")
@@ -596,6 +603,15 @@ def _extract_prompt(
                     formatted_messages.append(msg)
 
             if self.request_format == "plain":
+                if (
+                    self._engine is not None
+                    and getattr(self._engine, "tokenizer", None) is not None
+                ):
+                    logger.warning(
+                        "Tokenizer is set (from model) but not used: request_format is "
+                        "'plain', so chat prompt is built by string concatenation "
+                        "instead of the tokenizer's chat template."
+                    )
                 # No chat template: append message content only (e.g. "User: ...\nAssistant: ")
                 parts = []
                 for msg in formatted_messages:
@@ -630,7 +646,7 @@ def _extract_prompt(
             )
             if isinstance(prompt, str):
                 return prompt
-            raise RuntimeError("Code not setup for state")
+            raise RuntimeError("Backend received unexpected type from tokenizer.")
 
         if mode == "audio":
             prompt = body.get("prompt", "")

From 1c2f7cd815d268ae159e58e274c3ec4d0bcef77a Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 22:14:21 -0500
Subject: [PATCH 41/56] Remove unhelpful error message

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 60c9914e4..73abc1e25 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -603,15 +603,6 @@ def _extract_prompt(
                     formatted_messages.append(msg)
 
             if self.request_format == "plain":
-                if (
-                    self._engine is not None
-                    and getattr(self._engine, "tokenizer", None) is not None
-                ):
-                    logger.warning(
-                        "Tokenizer is set (from model) but not used: request_format is "
-                        "'plain', so chat prompt is built by string concatenation "
-                        "instead of the tokenizer's chat template."
-                    )
                 # No chat template: append message content only (e.g. "User: ...\nAssistant: ")
                 parts = []
                 for msg in formatted_messages:

From 056ef9f9cac1215728d9444d6007cb313e5953bb Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 23:15:55 -0500
Subject: [PATCH 42/56] Remove default value for max model length in vllm
 python backend

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 73abc1e25..7cb1878cd 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -123,7 +123,6 @@ def requires_model(cls) -> bool:
     DEFAULT_VLLM_CONFIG: dict[str, Any] = {
         "tensor_parallel_size": 1,
         "gpu_memory_utilization": 0.9,
-        "max_model_len": 2048,
     }
 
     def __init__(

From 60fa86f9921a73849ec2655f15036ce1c9ea6fbf Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 11 Feb 2026 23:29:40 -0500
Subject: [PATCH 43/56] Cleanup to prepare branch for review

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile.vllm                        | 90 -----------------------
 pyproject.toml                            |  6 +-
 scripts/lock.sh                           |  4 +-
 src/guidellm/backends/openai/http.py      |  4 -
 src/guidellm/backends/vllm_python/vllm.py |  6 +-
 src/guidellm/schemas/info.py              |  4 -
 src/guidellm/schemas/request_stats.py     | 21 ++----
 7 files changed, 12 insertions(+), 123 deletions(-)
 delete mode 100644 Containerfile.vllm

diff --git a/Containerfile.vllm b/Containerfile.vllm
deleted file mode 100644
index 638abfa4d..000000000
--- a/Containerfile.vllm
+++ /dev/null
@@ -1,90 +0,0 @@
-ARG BASE_IMAGE=vllm/vllm-openai
-
-FROM $BASE_IMAGE
-
-# Metadata
-LABEL io.k8s.display-name="GuideLLM" \
-      org.opencontainers.image.description="GuideLLM Performance Benchmarking Container" \
-      org.opencontainers.image.source="https://github.com/vllm-project/guidellm" \
-      org.opencontainers.image.documentation="https://blog.vllm.ai/guidellm/stable" \
-      org.opencontainers.image.license="Apache-2.0"
-
-# release: take the last version and add a post if build iteration
-# candidate: increment to next minor, add 'rc' with build iteration
-# nightly: increment to next minor, add 'a' with build iteration
-# alpha: increment to next minor, add 'a' with build iteration
-# dev: increment to next minor, add 'dev' with build iteration
-ARG GUIDELLM_BUILD_TYPE=dev
-
-# Extra dependencies to install
-# all: install all extras
-# recommended: install recommended extras
-# container: All extras except vllm
-ARG GUIDELLM_BUILD_EXTRAS=container
-
-# Switch to root for installing packages
-USER root
-
-RUN apt-get update || true \
-    && apt-get install -y --no-install-recommends gnupg ca-certificates \
-    && apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 871920D1991BC93C \
-    && apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys BA6932366A755776 \
-    && rm -rf /var/lib/apt/lists/*
-
-RUN apt-get update && apt-get install -y git \
-    && rm -rf /var/lib/apt/lists/*
-
-# Set correct build type for versioning
-# Configure uv for building guidellm
-ENV GUIDELLM_BUILD_TYPE=$GUIDELLM_BUILD_TYPE \
-    VIRTUAL_ENV=/opt/app-root \
-    UV_PROJECT="/src" \
-    UV_LINK_MODE="copy" \
-    UV_NO_DEV="1" \
-    UV_NO_EDITABLE="1" \
-    UV_FROZEN="1" \
-    UV_CACHE_DIR="/tmp/uv_cache"
-
-# Sync initial environment
-RUN --mount=type=cache,target=$UV_CACHE_DIR \
-    --mount=type=bind,source=uv.lock,target=$UV_PROJECT/uv.lock,relabel=shared \
-    --mount=type=bind,source=pyproject.toml,target=$UV_PROJECT/pyproject.toml,relabel=shared \
-    uv sync --active --no-install-project --extra $GUIDELLM_BUILD_EXTRAS --system-site-packages
-
-# Copy repository files
-# Do this as late as possible to leverage layer caching
-COPY / $UV_PROJECT
-
-# Install guidellm
-RUN --mount=type=cache,target=$UV_CACHE_DIR \
-    uv sync --active --extra $GUIDELLM_BUILD_EXTRAS
-
-
-# Prod image
-#FROM $BASE_IMAGE
-
-# Switch to root for installing packages
-#USER root
-
-# Install some helpful utilities and deps
-RUN apt-get update && apt-get install -y --no-install-recommends \
-        vim tar rsync ffmpeg \
-        && apt clean
-
-# Add guidellm bin to PATH
-# Argument defaults can be set with GUIDELLM_<ARG>
-ENV HOME="/home/guidellm" \
-    GUIDELLM_OUTPUT_DIR="/results"
-
-# Create the user home dir
-WORKDIR $HOME
-
-# Create a volume for results
-VOLUME /results
-
-# Switch back to unpriv user
-# Root group for k8s
-USER 1001:0
-
-ENTRYPOINT [ "/opt/app-root/bin/guidellm" ]
-CMD [ "benchmark", "run" ]
diff --git a/pyproject.toml b/pyproject.toml
index 0d3b50f9c..27bea0625 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,7 +18,6 @@ explicit = true
 
 [tool.uv.sources]
 torch = { index = "pytorch-cpu" }
-torchvision = { index = "pytorch-cpu" }
 
 # ************************************************
 # ********** Project Metadata **********
@@ -68,7 +67,9 @@ dependencies = [
     "rich",
     "sanic",
     "tabulate",
+    "transformers",
     "uvloop>=0.18",
+    "torch",
 ]
 
 [project.optional-dependencies]
@@ -82,13 +83,12 @@ audio = [
     # Lowest version with full torchcodec support
     "datasets[audio]>=4.1.0",
     # Torchcodec needs specific torch version
-    # Note: torch is provided by pytorch extra or base image in containers
+    "torch==2.9.*",
     "torchcodec==0.8.*",
 ]
 vision = [
     "datasets[vision]",
     "pillow",
-    "torchvision",
 ]
 # Dev Tooling
 dev = [
diff --git a/scripts/lock.sh b/scripts/lock.sh
index 2bd31bebe..40daf839c 100755
--- a/scripts/lock.sh
+++ b/scripts/lock.sh
@@ -27,7 +27,7 @@ while getopts "fh" opt; do
 done
 
 if [ $FORCE_REGEN -eq 1 ]; then
-    uv sync --all-extras --no-install-project --upgrade
+    uv lock -U
 else
-    uv sync --all-extras --no-install-project
+    uv lock
 fi
diff --git a/src/guidellm/backends/openai/http.py b/src/guidellm/backends/openai/http.py
index b571c22ea..86bdb0e3d 100644
--- a/src/guidellm/backends/openai/http.py
+++ b/src/guidellm/backends/openai/http.py
@@ -358,7 +358,6 @@ async def resolve(  # type: ignore[override]
 
         try:
             request_info.timings.request_start = time.time()
-            request_info.timings.output_token_iteration_timings = []
 
             async with self._async_client.stream(
                 arguments.method or "POST",
@@ -391,9 +390,6 @@ async def resolve(  # type: ignore[override]
 
                     request_info.timings.last_token_iteration = iter_time
                     request_info.timings.token_iterations += iterations
-                    request_info.timings.output_token_iteration_timings.append(
-                        (iter_time, float(iterations))
-                    )
 
             request_info.timings.request_end = time.time()
             yield request_handler.compile_streaming(request, arguments), request_info
diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 7cb1878cd..def67431f 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -850,8 +850,6 @@ async def resolve(  # type: ignore[override]
         accumulated_text = ""
         total_output_tokens = 0
         previous_token_count = 0
-        if stream:
-            request_info.timings.output_token_iteration_timings = []
 
         try:
             async for request_output in self._engine.generate(  # type: ignore[misc]
@@ -911,9 +909,7 @@ async def resolve(  # type: ignore[override]
                                 previous_token_count += 1
                             if chunk_token_count > 0:
                                 total_output_tokens += chunk_token_count
-                                request_info.timings.output_token_iteration_timings.append(
-                                    (iter_time, float(chunk_token_count))
-                                )
+                                
                             self._update_token_timing(request_info, iter_time, iterations)
 
                         accumulated_text = generated_text
diff --git a/src/guidellm/schemas/info.py b/src/guidellm/schemas/info.py
index bb60db128..e5481b276 100644
--- a/src/guidellm/schemas/info.py
+++ b/src/guidellm/schemas/info.py
@@ -71,10 +71,6 @@ class RequestTimings(StandardBaseDict):
     token_iterations: int = Field(
         default=0,
     )
-    output_token_iteration_timings: list[tuple[float, float]] | None = Field(
-        default=None,
-        description="Chronological (timestamp, token_count) per stream chunk when available",
-    )
     request_end: float | None = Field(
         default=None,
         description="Unix timestamp when the backend completed processing the request",
diff --git a/src/guidellm/schemas/request_stats.py b/src/guidellm/schemas/request_stats.py
index 8e5016a6b..e94e31c38 100644
--- a/src/guidellm/schemas/request_stats.py
+++ b/src/guidellm/schemas/request_stats.py
@@ -119,13 +119,12 @@ def output_tokens(self) -> int | None:
         """
         :return: Number of tokens in the generated output, or None if unavailable
         """
-        # Prefer total_tokens; fall back to text_tokens (e.g. from injected streaming usage)
-        # then token_iterations (assumes each iteration is one token)
-        if self.output_metrics.total_tokens is not None:
-            return self.output_metrics.total_tokens
-        if self.output_metrics.text_tokens is not None:
-            return self.output_metrics.text_tokens
-        return self.info.timings.token_iterations or None
+        # Fallback if we did not get usage metrics from the server
+        # NOTE: This assumes each iteration is one token
+        if self.output_metrics.total_tokens is None:
+            return self.info.timings.token_iterations or None
+
+        return self.output_metrics.total_tokens
 
     @computed_field  # type: ignore[misc]
     @property
@@ -289,10 +288,6 @@ def output_tokens_timings(self) -> list[tuple[float, float]]:
         :return: List of (timestamp, token_count) tuples for output token generations
         :raises ValueError: If resolve_end timings are not set
         """
-        real = self.info.timings.output_token_iteration_timings
-        if real is not None and len(real) > 0:
-            return [(float(t), float(n)) for t, n in real]
-
         if (
             self.first_token_iteration is None
             or self.last_token_iteration is None
@@ -321,10 +316,6 @@ def iter_tokens_timings(self) -> list[tuple[float, float]]:
         :return: List of (timestamp, token_count) tuples for iterations excluding
             first token
         """
-        real = self.info.timings.output_token_iteration_timings
-        if real is not None and len(real) > 1:
-            return [(float(t), float(n)) for t, n in real[1:]]
-
         if (
             self.first_token_iteration is None
             or self.last_token_iteration is None

From 8c58beb9f0d2ef383a2aa93f0f40fe0cda515953 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Thu, 12 Feb 2026 18:20:34 -0500
Subject: [PATCH 44/56] Remove specified defaults for vllm inputs

Generated-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 48 ++++++++---------------
 1 file changed, 17 insertions(+), 31 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index def67431f..5e44a9b54 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -81,15 +81,11 @@ class VLLMPythonBackend(Backend):
     conversion between GuideLLM schemas and VLLM's native API, with async support for
     finer token-by-token processing and timings.
 
-    Example:
+    Engine parameters not set in vllm_config use vLLM's AsyncEngineArgs defaults.
+    Example (optional overrides):
     ::
-        backend = VLLMPythonBackend(
-            model="meta-llama/Llama-2-7b-chat-hf",
-            vllm_config={
-                "tensor_parallel_size": 1,
-                "gpu_memory_utilization": 0.9,
-            }
-        )
+        backend = VLLMPythonBackend(model="meta-llama/Llama-2-7b-chat-hf")
+        # Or with overrides: vllm_config={"tensor_parallel_size": 1, "gpu_memory_utilization": 0.9}
 
         await backend.process_startup()
         async for response, request_info in backend.resolve(request, info):
@@ -119,12 +115,6 @@ def requires_model(cls) -> bool:
         """
         return True
 
-    # Default VLLM configuration
-    DEFAULT_VLLM_CONFIG: dict[str, Any] = {
-        "tensor_parallel_size": 1,
-        "gpu_memory_utilization": 0.9,
-    }
-
     def __init__(
         self,
         model: str,
@@ -137,13 +127,11 @@ def __init__(
         Initialize VLLM Python backend with model and configuration.
 
         :param model: Model identifier or path for VLLM to load
-        :param vllm_config: Dictionary of VLLM AsyncEngineArgs initialization parameters.
-            Merged with defaults. Common parameters include:
-            - tensor_parallel_size: Number of tensor parallel replicas
-            - gpu_memory_utilization: Fraction of GPU memory to use
-            - max_model_len: Maximum sequence length
-            - Any other parameter accepted by vllm.AsyncEngineArgs
-            Note: VLLM automatically uses CUDA if available, CPU otherwise.
+        :param vllm_config: Optional dict of VLLM AsyncEngineArgs parameters. Passed through
+            with no GuideLLM defaults; only model (and optionally chat_template) are set by
+            the backend. Unset parameters use vLLM's defaults. Common options include
+            tensor_parallel_size, gpu_memory_utilization, max_model_len, and any other
+            parameter accepted by vllm.AsyncEngineArgs. VLLM uses CUDA if available, else CPU.
             The 'device' parameter is not supported and will be ignored.
         :param request_format: "plain" (no chat template), "default-template" (use
             tokenizer default), or a chat template path / single-line string.
@@ -172,20 +160,18 @@ def processes_limit(self) -> int | None:
 
     def _merge_config(self, user_config: dict[str, Any]) -> dict[str, Any]:
         """
-        Merge user configuration with defaults.
+        Build engine config from user config plus required model and optional chat_template.
 
-        When request_format is a custom template (not plain or default-template),
-        adds chat_template to config for the vLLM engine if supported.
+        No GuideLLM defaults are applied; any parameter not set here or in user_config
+        is left to vLLM's AsyncEngineArgs defaults. When request_format is a custom
+        template (not plain or default-template), adds chat_template to config.
 
         :param user_config: User-provided configuration dictionary
-        :return: Merged configuration with defaults applied
+        :return: Config dict for AsyncEngineArgs (model always set; chat_template if applicable)
         """
-        config = self.DEFAULT_VLLM_CONFIG.copy()
+        config = dict(user_config)
 
-        # Merge user config, allowing overrides
-        config.update(user_config)
-
-        # Ensure model is set in config
+        # Ensure model is set in config (required; overrides user if they passed it)
         config["model"] = self.model
 
         # Pass custom chat template to vLLM engine when applicable
@@ -909,7 +895,7 @@ async def resolve(  # type: ignore[override]
                                 previous_token_count += 1
                             if chunk_token_count > 0:
                                 total_output_tokens += chunk_token_count
-                                
+
                             self._update_token_timing(request_info, iter_time, iterations)
 
                         accumulated_text = generated_text

From 8e7f8e4864898efbd5e2e31639f1cb1b7f580ffc Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Thu, 12 Feb 2026 18:38:35 -0500
Subject: [PATCH 45/56] Update the vllm backend to use more shared code

Generated-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py | 153 ++++++++++++----------
 1 file changed, 87 insertions(+), 66 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 5e44a9b54..e3e7425f2 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -366,31 +366,85 @@ def _update_token_timing(
         request_info.timings.last_token_iteration = iter_time
         request_info.timings.token_iterations += iterations
 
-    def _streaming_usage_from_output(
+    def _text_from_output(self, output: RequestOutput | None) -> str:
+        """
+        Extract generated text from VLLM RequestOutput.
+
+        :param output: VLLM request output (may be None)
+        :return: output.outputs[0].text if present, else ""
+        """
+        if output is None or not output.outputs or len(output.outputs) == 0:
+            return ""
+        return output.outputs[0].text or ""
+
+    def _openai_payload_for_mode(
+        self, mode: _Mode, text: str, *, is_delta: bool = False
+    ) -> dict[str, Any]:
+        """
+        Build OpenAI-style payload (content/choices only, no usage or id) for a mode.
+
+        :param mode: Inferred mode (audio, chat, text)
+        :param text: Content text (full or delta)
+        :param is_delta: If True, chat uses delta shape; else uses message shape
+        :return: Dict with "text" (audio) or "choices" (text/chat)
+        """
+        if mode == "audio":
+            return {"text": text}
+        if mode == "text":
+            return {"choices": [{"text": text}]}
+        # chat
+        if is_delta:
+            return {
+                "choices": [
+                    {"delta": {"content": text, "role": "assistant"}}
+                ]
+            }
+        return {
+            "choices": [
+                {"message": {"content": text, "role": "assistant"}}
+            ]
+        }
+
+    def _usage_from_output(
         self,
-        final_output: RequestOutput | None,
-        accumulated_text: str,
-        request_info: RequestInfo,
+        output: RequestOutput | None,
+        *,
         accumulated_output_tokens: int | None = None,
+        accumulated_text: str = "",
+        request_info: RequestInfo | None = None,
     ) -> dict[str, int] | None:
         """
-        Build usage dict (prompt_tokens, completion_tokens, total_tokens) for streaming.
+        Build usage dict (prompt_tokens, completion_tokens, total_tokens).
 
-        When accumulated_output_tokens is provided (from summing chunk token_ids in the
-        loop), uses that. Otherwise uses token_ids from final_output (last chunk only),
-        then tokenizer, then token_iterations fallback.
+        When request_info is None (non-stream), uses only output token counts.
+        When request_info is set (stream), uses accumulated_output_tokens if provided,
+        else token_ids from output, then tokenizer fallback, then token_iterations.
         """
-        if final_output is None:
+        if output is None:
             return None
-        input_tokens = len(final_output.prompt_token_ids)
+        input_tokens = len(output.prompt_token_ids)
         output_tokens = 0
+
+        if request_info is None:
+            # Simple path (non-stream): use output token counts only
+            if output.outputs and len(output.outputs) > 0:
+                out = output.outputs[0]
+                if out.token_ids is not None:
+                    output_tokens = len(out.token_ids)
+            return {
+                "prompt_tokens": input_tokens,
+                "completion_tokens": output_tokens,
+                "total_tokens": input_tokens + output_tokens,
+            }
+
+        # Stream path: fallback chain
         source = "unknown"
         generated_text = accumulated_text
         if accumulated_output_tokens is not None and accumulated_output_tokens > 0:
             output_tokens = accumulated_output_tokens
             source = "token_ids"
-        elif final_output.outputs and len(final_output.outputs) > 0:
-            out = final_output.outputs[0]
+        elif output.outputs and len(output.outputs) > 0:
+            out = output.outputs[0]
             if not generated_text and out.text:
                 generated_text = out.text
             if out.token_ids is not None:
@@ -452,11 +506,11 @@ def _build_final_response(
         if final_output is None:
             return None
         if stream:
-            usage = self._streaming_usage_from_output(
+            usage = self._usage_from_output(
                 final_output,
-                accumulated_text,
-                request_info,
                 accumulated_output_tokens=total_output_tokens,
+                accumulated_text=accumulated_text,
+                request_info=request_info,
             )
             if usage is not None:
                 response_handler.streaming_usage = usage
@@ -689,48 +743,15 @@ def _convert_vllm_output_to_openai_format(
         :param mode: Inferred mode (audio, chat, text) for response shape
         :return: OpenAI-style response dictionary
         """
-        # Extract generated text
-        generated_text = ""
-        if output.outputs and len(output.outputs) > 0:
-            generated_text = output.outputs[0].text
-
-        # Calculate token counts
-        input_tokens = len(output.prompt_token_ids)
-        output_tokens = 0
-        if output.outputs and len(output.outputs) > 0:
-            output_obj = output.outputs[0]
-            if output_obj.token_ids is not None:
-                output_tokens = len(output_obj.token_ids)
+        generated_text = self._text_from_output(output)
+        usage = self._usage_from_output(output) or {}
 
-        # Build usage dict
-        usage: dict[str, int] = {
-            "prompt_tokens": input_tokens,
-            "completion_tokens": output_tokens,
-            "total_tokens": input_tokens + output_tokens,
+        response_dict: dict[str, Any] = {
+            **self._openai_payload_for_mode(mode, generated_text, is_delta=False),
+            "usage": usage,
         }
-
-        if mode == "audio":
-            response_dict: dict[str, Any] = {
-                "text": generated_text,
-                "usage": usage,
-            }
-        elif mode == "text":
-            response_dict = {
-                "choices": [{"text": generated_text}],
-                "usage": usage,
-            }
-        else:
-            response_dict = {
-                "choices": [
-                    {"message": {"content": generated_text, "role": "assistant"}}
-                ],
-                "usage": usage,
-            }
-
-        # Add response ID if available
         if hasattr(output, "request_id") and output.request_id:
             response_dict["id"] = output.request_id
-
         return response_dict
 
     async def resolve(  # type: ignore[override]
@@ -841,10 +862,12 @@ async def resolve(  # type: ignore[override]
             async for request_output in self._engine.generate(  # type: ignore[misc]
                 prompt, sampling_params, request_id
             ):
+                # Per-iteration: record timing and keep latest output for final response
                 iter_time = time.time()
                 self._update_request_timing(request_info, iter_time)
                 final_output = request_output
 
+                # Stop when vLLM signals completion (e.g. EOS or max_tokens)
                 if (
                     request_output.outputs
                     and len(request_output.outputs) > 0
@@ -852,38 +875,35 @@ async def resolve(  # type: ignore[override]
                 ):
                     break
 
+                # Streaming: emit new text as SSE deltas and accumulate token count
                 if stream:
-                    generated_text = ""
-                    if request_output.outputs and len(request_output.outputs) > 0:
-                        generated_text = request_output.outputs[0].text
+                    generated_text = self._text_from_output(request_output)
 
                     if generated_text != accumulated_text:
+                        # Only the new slice since last iteration (vLLM gives cumulative text)
                         delta_text = generated_text[len(accumulated_text) :]
-
-                        if ctx.mode == "text":
-                            delta_data = {"choices": [{"text": delta_text}]}
-                        elif ctx.mode == "audio":
-                            delta_data = {"text": delta_text}
-                        else:
-                            delta_data = {
-                                "choices": [{"delta": {"content": delta_text, "role": "assistant"}}]
-                            }
-
+                        # Build OpenAI-style delta: {"text": ...}, choices[].text, or choices[].delta
+                        delta_data = self._openai_payload_for_mode(
+                            ctx.mode, delta_text, is_delta=True
+                        )
                         if hasattr(request_output, "request_id") and request_output.request_id:
                             delta_data["id"] = request_output.request_id
 
+                        # Serialize to SSE line and feed to handler (accumulates for compile_streaming)
                         json_str = json.dumps(delta_data)
                         if isinstance(json_str, bytes):
                             json_str = json_str.decode("utf-8")
                         sse_line = f"data: {json_str}"
                         iterations = response_handler.add_streaming_line(sse_line)
 
+                        # Update token count (delta from cumulative token_ids) and token timings
                         if iterations is not None and iterations > 0:
                             out = (
                                 request_output.outputs[0]
                                 if request_output.outputs
                                 else None
                             )
+                            # vLLM yields cumulative token_ids; take delta since previous iteration
                             if out is not None and out.token_ids is not None:
                                 current_count = len(out.token_ids)
                                 chunk_token_count = max(
@@ -898,6 +918,7 @@ async def resolve(  # type: ignore[override]
 
                             self._update_token_timing(request_info, iter_time, iterations)
 
+                        # Track consumed text so next delta is again only the new slice
                         accumulated_text = generated_text
 
             request_info.timings.request_end = time.time()

From 084dea655d6813e78c34135efd4e54c3ab733c2d Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 16 Feb 2026 12:46:03 -0500
Subject: [PATCH 46/56] Refactor code to fix linter errors

Assisted-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py     | 750 ++++++++++--------
 .../backends/vllm_python/vllm_response.py     |  47 +-
 2 files changed, 432 insertions(+), 365 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index e3e7425f2..36726b28c 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -12,18 +12,27 @@
 import asyncio
 import time
 import uuid
-from pathlib import Path
 from collections.abc import AsyncIterator
-from typing import Any, Literal
-
-_Mode = Literal["audio", "chat", "text"]
+from pathlib import Path
+from typing import Any, Literal, cast
 
 import numpy as np
 
+from guidellm.backends.backend import Backend
+from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler
+from guidellm.extras.audio import _decode_audio
+from guidellm.logger import logger
+from guidellm.schemas import (
+    GenerationRequest,
+    GenerationResponse,
+    RequestInfo,
+)
+from guidellm.utils import json
+
 try:
     from vllm import SamplingParams
-    from vllm.engine.async_llm_engine import AsyncLLMEngine
     from vllm.engine.arg_utils import AsyncEngineArgs
+    from vllm.engine.async_llm_engine import AsyncLLMEngine
     from vllm.outputs import RequestOutput
 
     HAS_VLLM = True
@@ -34,12 +43,12 @@
     RequestOutput = None  # type: ignore[assignment, misc]
     HAS_VLLM = False
 
-from guidellm.logger import logger
-from guidellm.backends.backend import Backend
-from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler
-from guidellm.extras.audio import _decode_audio
-from guidellm.schemas import GenerationRequest, GenerationResponse, RequestInfo, UsageMetrics
-from guidellm.utils import json
+_Mode = Literal["audio", "chat", "text"]
+
+# Audio file tuple: (path_or_name, data_bytes) or (path_or_name, data_bytes, mimetype)
+_AUDIO_FILE_MIN_LEN = 2
+_AUDIO_FILE_DATA_INDEX = 1
+_AUDIO_FILE_MIMETYPE_INDEX = 2
 
 __all__ = ["VLLMPythonBackend"]
 
@@ -66,8 +75,8 @@ def _check_vllm_available() -> None:
     """Check if vllm is available and raise helpful error if not."""
     if not HAS_VLLM:
         raise ImportError(
-            "vllm is not installed. Please install it using 'pip install guidellm[vllm]' "
-            "or 'pip install vllm>=0.6.0'"
+            "vllm is not installed. Please install it using "
+            "'pip install guidellm[vllm]' or 'pip install vllm>=0.6.0'"
         )
 
 
@@ -85,7 +94,7 @@ class VLLMPythonBackend(Backend):
     Example (optional overrides):
     ::
         backend = VLLMPythonBackend(model="meta-llama/Llama-2-7b-chat-hf")
-        # Or with overrides: vllm_config={"tensor_parallel_size": 1, "gpu_memory_utilization": 0.9}
+        # Or: vllm_config={"tensor_parallel_size": 1, "gpu_memory_utilization": 0.9}
 
         await backend.process_startup()
         async for response, request_info in backend.resolve(request, info):
@@ -120,27 +129,29 @@ def __init__(
         model: str,
         vllm_config: dict[str, Any] | None = None,
         request_format: str | None = None,
-        target: str | None = None,  # Ignored for VLLM Python backend
+        target: str | None = None,  # Backend.create API; unused (local)
         stream: bool = True,
     ):
         """
         Initialize VLLM Python backend with model and configuration.
 
         :param model: Model identifier or path for VLLM to load
-        :param vllm_config: Optional dict of VLLM AsyncEngineArgs parameters. Passed through
-            with no GuideLLM defaults; only model (and optionally chat_template) are set by
-            the backend. Unset parameters use vLLM's defaults. Common options include
-            tensor_parallel_size, gpu_memory_utilization, max_model_len, and any other
-            parameter accepted by vllm.AsyncEngineArgs. VLLM uses CUDA if available, else CPU.
+        :param vllm_config: Optional dict of VLLM AsyncEngineArgs parameters.
+            Passed through with no GuideLLM defaults; only model (and optionally
+            chat_template) are set by the backend. Unset parameters use vLLM's
+            defaults. Common options include tensor_parallel_size,
+            gpu_memory_utilization, max_model_len, and any other parameter
+            accepted by vllm.AsyncEngineArgs. VLLM uses CUDA if available, else CPU.
             The 'device' parameter is not supported and will be ignored.
-        :param request_format: "plain" (no chat template), "default-template" (use
-            tokenizer default), or a chat template path / single-line string.
-        :param target: Target URL (ignored for VLLM Python backend, which runs locally)
+        :param request_format: "plain" (no chat template), "default-template"
+            (use tokenizer default), or a chat template path / single-line string.
+        :param target: Target URL (ignored for VLLM Python backend, runs locally)
         :param stream: Whether to stream responses (default True). Can be overridden per
             request via request.arguments.stream.
         """
         _check_vllm_available()
         super().__init__(type_="vllm_python")
+        _ = target  # Required by Backend.create(); unused for local backend
 
         self.model = model
         self.request_format = request_format
@@ -154,20 +165,23 @@ def __init__(
     @property
     def processes_limit(self) -> int | None:
         """
-        Limits VLLM Python to a single process, since it starts up an entire VLLM engine.
+        Limits VLLM Python to a single process, since it starts up an entire
+        VLLM engine.
         """
         return 1
 
     def _merge_config(self, user_config: dict[str, Any]) -> dict[str, Any]:
         """
-        Build engine config from user config plus required model and optional chat_template.
+        Build engine config from user config plus required model and optional
+        chat_template.
 
-        No GuideLLM defaults are applied; any parameter not set here or in user_config
-        is left to vLLM's AsyncEngineArgs defaults. When request_format is a custom
-        template (not plain or default-template), adds chat_template to config.
+        No GuideLLM defaults are applied; any parameter not set here or in
+        user_config is left to vLLM's AsyncEngineArgs defaults. When
+        request_format is a custom template (not plain or default-template),
+        adds chat_template to config.
 
         :param user_config: User-provided configuration dictionary
-        :return: Config dict for AsyncEngineArgs (model always set; chat_template if applicable)
+        :return: Config dict for AsyncEngineArgs (model set; chat_template if set)
         """
         config = dict(user_config)
 
@@ -175,9 +189,9 @@ def _merge_config(self, user_config: dict[str, Any]) -> dict[str, Any]:
         config["model"] = self.model
 
         # Pass custom chat template to vLLM engine when applicable
-        if (
-            self.request_format is not None
-            and self.request_format not in ("plain", "default-template")
+        if self.request_format is not None and self.request_format not in (
+            "plain",
+            "default-template",
         ):
             config["chat_template"] = self.request_format
 
@@ -246,7 +260,7 @@ async def validate(self):
             async for _ in self._engine.generate("test", test_params, request_id):  # type: ignore[misc]
                 # Just consume the first output to verify it works
                 break
-        except Exception as exc:
+        except (RuntimeError, ValueError, TypeError, OSError) as exc:
             raise RuntimeError(
                 "Backend validation failed. VLLM model could not generate text."
             ) from exc
@@ -314,10 +328,12 @@ def _get_request_context(self, request: GenerationRequest) -> _RequestContext:
                     messages.append({"role": "system", "content": str(p)})
             for t in text_parts:
                 if t:
-                    messages.append({
-                        "role": "user",
-                        "content": [{"type": "text", "text": str(t)}],
-                    })
+                    messages.append(
+                        {
+                            "role": "user",
+                            "content": [{"type": "text", "text": str(t)}],
+                        }
+                    )
             body = {"messages": messages}
             stream = self.stream
             files = {}
@@ -326,18 +342,20 @@ def _get_request_context(self, request: GenerationRequest) -> _RequestContext:
             mode: _Mode = "audio"
         elif body.get("messages"):
             mode = "chat"
-        elif body.get("prompt") is not None and body.get("prompt") != "":
-            mode = "text"
-        elif "prompt" in body:
+        elif (
+            body.get("prompt") is not None
+            and body.get("prompt") != ""
+            or "prompt" in body
+        ):
             mode = "text"
         else:
-            raise ValueError(
-                "Request must include prompt, messages, or audio files."
-            )
+            raise ValueError("Request must include prompt, messages, or audio files.")
 
         return _RequestContext(mode=mode, body=body, stream=stream, files=files)
 
-    def _update_request_timing(self, request_info: RequestInfo, iter_time: float) -> None:
+    def _update_request_timing(
+        self, request_info: RequestInfo, iter_time: float
+    ) -> None:
         """
         Update request iteration timing information.
 
@@ -394,52 +412,25 @@ def _openai_payload_for_mode(
             return {"choices": [{"text": text}]}
         # chat
         if is_delta:
-            return {
-                "choices": [
-                    {"delta": {"content": text, "role": "assistant"}}
-                ]
-            }
-        return {
-            "choices": [
-                {"message": {"content": text, "role": "assistant"}}
-            ]
-        }
+            return {"choices": [{"delta": {"content": text, "role": "assistant"}}]}
+        return {"choices": [{"message": {"content": text, "role": "assistant"}}]}
 
-    def _usage_from_output(
+    def _stream_usage_tokens(
         self,
-        output: RequestOutput | None,
-        *,
-        accumulated_output_tokens: int | None = None,
-        accumulated_text: str = "",
-        request_info: RequestInfo | None = None,
-    ) -> dict[str, int] | None:
+        output: RequestOutput,
+        accumulated_output_tokens: int | None,
+        accumulated_text: str,
+        request_info: RequestInfo,
+    ) -> tuple[int, int]:
         """
-        Build usage dict (prompt_tokens, completion_tokens, total_tokens).
-
-        When request_info is None (non-stream), uses only output token counts.
-        When request_info is set (stream), uses accumulated_output_tokens if provided,
-        else token_ids from output, then tokenizer fallback, then token_iterations.
+        Stream path: compute output_tokens; return input and output token counts
+        in the form of (input_tokens, output_tokens).
         """
-        if output is None:
-            return None
-        input_tokens = len(output.prompt_token_ids)
+        input_tokens = len(output.prompt_token_ids or [])
         output_tokens = 0
-
-        if request_info is None:
-            # Simple path (non-stream): use output token counts only
-            if output.outputs and len(output.outputs) > 0:
-                out = output.outputs[0]
-                if out.token_ids is not None:
-                    output_tokens = len(out.token_ids)
-            return {
-                "prompt_tokens": input_tokens,
-                "completion_tokens": output_tokens,
-                "total_tokens": input_tokens + output_tokens,
-            }
-
-        # Stream path: fallback chain
         source = "unknown"
         generated_text = accumulated_text
+
         if accumulated_output_tokens is not None and accumulated_output_tokens > 0:
             output_tokens = accumulated_output_tokens
             source = "token_ids"
@@ -452,8 +443,8 @@ def _usage_from_output(
                 source = "token_ids"
             else:
                 logger.debug(
-                    "[vllm_python streaming] final_output.outputs[0].token_ids is None, "
-                    "len(generated_text)={}",
+                    "[vllm_python streaming] final_output.outputs[0].token_ids "
+                    "is None, len(generated_text)={}",
                     len(generated_text or ""),
                 )
         if output_tokens == 0 and generated_text and self._engine is not None:
@@ -469,7 +460,7 @@ def _usage_from_output(
                     logger.debug(
                         "[vllm_python streaming] engine has no tokenizer attribute"
                     )
-            except Exception as exc:
+            except (TypeError, ValueError, RuntimeError) as exc:
                 logger.debug(
                     "[vllm_python streaming] tokenizer.encode failed: {}",
                     exc,
@@ -485,6 +476,42 @@ def _usage_from_output(
             input_tokens,
             request_info.timings.token_iterations,
         )
+        return input_tokens, output_tokens
+
+    def _usage_from_output(
+        self,
+        output: RequestOutput | None,
+        *,
+        accumulated_output_tokens: int | None = None,
+        accumulated_text: str = "",
+        request_info: RequestInfo | None = None,
+    ) -> dict[str, int] | None:
+        """
+        Build usage dict (prompt_tokens, completion_tokens, total_tokens).
+
+        When request_info is None (non-stream), uses only output token counts.
+        When request_info is set (stream), uses fallback chain via
+        _stream_usage_tokens.
+        """
+        if output is None:
+            return None
+        input_tokens = len(output.prompt_token_ids or [])
+        output_tokens = 0
+
+        if request_info is None:
+            if output.outputs and len(output.outputs) > 0:
+                out = output.outputs[0]
+                if out.token_ids is not None:
+                    output_tokens = len(out.token_ids)
+            return {
+                "prompt_tokens": input_tokens,
+                "completion_tokens": output_tokens,
+                "total_tokens": input_tokens + output_tokens,
+            }
+
+        input_tokens, output_tokens = self._stream_usage_tokens(
+            output, accumulated_output_tokens, accumulated_text, request_info
+        )
         return {
             "prompt_tokens": input_tokens,
             "completion_tokens": output_tokens,
@@ -518,9 +545,13 @@ def _build_final_response(
         openai_format = self._convert_vllm_output_to_openai_format(
             final_output, ctx.mode
         )
-        return response_handler.compile_non_streaming(request, openai_format), request_info
+        return response_handler.compile_non_streaming(
+            request, openai_format
+        ), request_info
 
-    def _extract_text_from_content(self, content: str | list[dict[str, Any]] | Any) -> str:
+    def _extract_text_from_content(
+        self, content: str | list[dict[str, Any]] | Any
+    ) -> str:
         """
         Extract text content from message content field.
 
@@ -547,9 +578,7 @@ def _extract_text_from_content(self, content: str | list[dict[str, Any]] | Any)
         # Fallback: convert to string
         return str(content) if content is not None else ""
 
-    def _extract_audio_from_request(
-        self, files: dict[str, Any]
-    ) -> tuple[bytes, str]:
+    def _extract_audio_from_request(self, files: dict[str, Any]) -> tuple[bytes, str]:
         """
         Extract audio file from request files dict.
 
@@ -561,35 +590,116 @@ def _extract_audio_from_request(
         :raises ValueError: If files is empty or no valid audio file found
         """
         if not files:
-            raise ValueError(
-                "Audio request must include audio file in 'files'."
-            )
+            raise ValueError("Audio request must include audio file in 'files'.")
 
         audio_file = None
         for value in files.values():
-            if isinstance(value, (list, tuple)) and len(value) >= 2:
+            if isinstance(value, list | tuple) and len(value) >= _AUDIO_FILE_MIN_LEN:
                 audio_file = value
                 break
-            elif isinstance(value, bytes):
+            if isinstance(value, bytes):
                 audio_file = ("audio.wav", value, "audio/wav")
                 break
 
         if audio_file is None:
-            raise ValueError(
-                "Audio request must include audio file in 'files'."
-            )
+            raise ValueError("Audio request must include audio file in 'files'.")
 
-        if isinstance(audio_file, (list, tuple)) and len(audio_file) >= 2:
-            audio_data = audio_file[1]
+        is_audio_tuple = (
+            isinstance(audio_file, list | tuple)
+            and len(audio_file) >= _AUDIO_FILE_MIN_LEN
+        )
+        if is_audio_tuple:
+            audio_data = audio_file[_AUDIO_FILE_DATA_INDEX]
             if not isinstance(audio_data, bytes):
                 raise ValueError(
-                    f"Expected bytes for audio data, got {type(audio_data)}: {audio_data}"
+                    f"Expected bytes for audio data, got {type(audio_data)}: "
+                    f"{audio_data}"
                 )
-            mimetype = audio_file[2] if len(audio_file) >= 3 else "audio/wav"
+            mimetype = (
+                audio_file[_AUDIO_FILE_MIMETYPE_INDEX]
+                if len(audio_file) > _AUDIO_FILE_MIMETYPE_INDEX
+                else "audio/wav"
+            )
             return (audio_data, mimetype)
 
         raise ValueError("Invalid audio file format in 'files'.")
 
+    def _extract_prompt_text(self, body: dict[str, Any]) -> str:
+        """Extract prompt for text mode."""
+        if (
+            self._engine is not None
+            and getattr(self._engine, "tokenizer", None) is not None
+        ):
+            logger.warning(
+                "Tokenizer is set (from model) but not used: request has 'prompt' "
+                "so mode was inferred as text; prompt is passed through without "
+                "tokenizer formatting."
+            )
+        prompt = body.get("prompt", "")
+        if not prompt:
+            raise ValueError("Text request must include 'prompt' in body.")
+        return prompt if isinstance(prompt, str) else str(prompt)
+
+    def _extract_prompt_chat_plain(
+        self, formatted_messages: list[dict[str, Any]]
+    ) -> str:
+        """Format messages as plain 'Role: content' lines."""
+        parts = []
+        for msg in formatted_messages:
+            role = msg.get("role", "user")
+            content = msg.get("content", "")
+            if role and content:
+                parts.append(f"{role.capitalize()}: {content}")
+        parts.append("Assistant: ")
+        return "\n".join(parts)
+
+    def _extract_prompt_chat_tokenizer(
+        self, formatted_messages: list[dict[str, Any]]
+    ) -> str:
+        """Apply tokenizer chat template to formatted messages."""
+        if self._engine is None:
+            raise RuntimeError("Backend not started up while extracting prompt.")
+        tokenizer = self._engine.tokenizer
+        if tokenizer is None:
+            raise RuntimeError("Backend engine has no tokenizer.")
+        if self.request_format is not None and self.request_format not in (
+            "plain",
+            "default-template",
+        ):
+            template_value = self.request_format
+            path = Path(template_value)
+            if path.exists() and path.is_file():
+                tokenizer.chat_template = path.read_text()  # type: ignore[attr-defined]
+            else:
+                tokenizer.chat_template = template_value  # type: ignore[assignment, attr-defined]
+        prompt = tokenizer.apply_chat_template(
+            formatted_messages,  # type: ignore[arg-type]
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+        if isinstance(prompt, str):
+            return prompt
+        raise RuntimeError("Backend received unexpected type from tokenizer.")
+
+    def _extract_prompt_chat(self, body: dict[str, Any]) -> str:
+        """Extract prompt for chat mode (plain or tokenizer)."""
+        messages = body.get("messages", [])
+        if not messages:
+            raise ValueError("Chat request must include 'messages' in body.")
+        formatted_messages = []
+        for msg in messages:
+            if isinstance(msg, dict):
+                raw_content = msg.get("content", "")
+                text_content = self._extract_text_from_content(raw_content)
+                formatted_messages.append(
+                    {"role": msg.get("role", ""), "content": text_content}
+                )
+            else:
+                formatted_messages.append(msg)
+        if self.request_format == "plain":
+            return self._extract_prompt_chat_plain(formatted_messages)
+        return self._extract_prompt_chat_tokenizer(formatted_messages)
+
     def _extract_prompt(
         self,
         body: dict[str, Any],
@@ -598,8 +708,9 @@ def _extract_prompt(
         """
         Extract prompt text from request body based on mode.
 
-        For chat mode, uses the tokenizer's chat template or plain concat per
-        request_format. For text mode uses body prompt. For audio, prompt is optional.
+        For chat mode, uses tokenizer chat template or plain concat per
+        request_format. For text mode uses body prompt. For audio, prompt
+        is optional.
 
         :param body: Request body dict (prompt or messages)
         :param mode: Inferred mode (audio, chat, text)
@@ -607,80 +718,14 @@ def _extract_prompt(
         :raises ValueError: If prompt cannot be extracted
         """
         if mode == "text":
-            if (
-                self._engine is not None
-                and getattr(self._engine, "tokenizer", None) is not None
-            ):
-                logger.warning(
-                    "Tokenizer is set (from model) but not used: request has 'prompt' "
-                    "so mode was inferred as text; prompt is passed through without "
-                    "tokenizer formatting."
-                )
-            prompt = body.get("prompt", "")
-            if not prompt:
-                raise ValueError("Text request must include 'prompt' in body.")
-            return prompt if isinstance(prompt, str) else str(prompt)
-
+            return self._extract_prompt_text(body)
         if mode == "chat":
-            messages = body.get("messages", [])
-            if not messages:
-                raise ValueError(
-                    "Chat request must include 'messages' in body."
-                )
-
-            # Convert messages to format expected by chat template or plain concat
-            formatted_messages = []
-            for msg in messages:
-                if isinstance(msg, dict):
-                    raw_content = msg.get("content", "")
-                    text_content = self._extract_text_from_content(raw_content)
-                    formatted_messages.append({
-                        "role": msg.get("role", ""),
-                        "content": text_content,
-                    })
-                else:
-                    formatted_messages.append(msg)
-
-            if self.request_format == "plain":
-                # No chat template: append message content only (e.g. "User: ...\nAssistant: ")
-                parts = []
-                for msg in formatted_messages:
-                    role = msg.get("role", "user")
-                    content = msg.get("content", "")
-                    if role and content:
-                        parts.append(f"{role.capitalize()}: {content}")
-                parts.append("Assistant: ")
-                return "\n".join(parts)
-
-            # default-template, None, or custom template: use tokenizer
-            if self._engine is None:
-                raise RuntimeError("Backend not started up while extracting prompt.")
-            tokenizer = self._engine.tokenizer
-
-            if (
-                self.request_format is not None
-                and self.request_format not in ("plain", "default-template")
-            ):
-                # Custom template: set on tokenizer if not already passed via engine config
-                template_value = self.request_format
-                path = Path(template_value)
-                if path.exists() and path.is_file():
-                    tokenizer.chat_template = path.read_text()
-                else:
-                    tokenizer.chat_template = template_value  # type: ignore[assignment]
-
-            prompt = tokenizer.apply_chat_template(  # type: ignore[misc]
-                formatted_messages,
-                tokenize=False,
-                add_generation_prompt=True,
-            )
-            if isinstance(prompt, str):
-                return prompt
-            raise RuntimeError("Backend received unexpected type from tokenizer.")
-
+            return self._extract_prompt_chat(body)
         if mode == "audio":
             prompt = body.get("prompt", "")
-            return prompt if isinstance(prompt, str) else str(prompt) if prompt else ""
+            return (
+                prompt if isinstance(prompt, str) else str(prompt) if prompt else ""
+            )
         raise ValueError(f"Unsupported mode: {mode!r}.")
 
     def _create_sampling_params(
@@ -692,7 +737,7 @@ def _create_sampling_params(
         Create VLLM SamplingParams from request body.
 
         :param body: Request body dict with sampling parameters
-        :param max_tokens_override: Optional max_tokens from request (e.g. benchmark target)
+        :param max_tokens_override: Optional max_tokens from request (e.g. benchmark)
         :return: Configured SamplingParams instance
         """
         max_tokens = (
@@ -711,7 +756,7 @@ def _create_sampling_params(
             stop: list[str] | None = []
         else:
             ignore_eos = body.get("ignore_eos", False)
-            stop = body.get("stop", None)
+            stop = body.get("stop")
 
         params = {
             "temperature": body.get("temperature", 1.0),
@@ -754,7 +799,161 @@ def _convert_vllm_output_to_openai_format(
             response_dict["id"] = output.request_id
         return response_dict
 
-    async def resolve(  # type: ignore[override]
+    def _build_audio_generate_input(
+        self, prompt: str, ctx: _RequestContext
+    ) -> dict[str, Any]:
+        """Decode audio from request and build multimodal generate_input dict."""
+        audio_bytes, _mimetype = self._extract_audio_from_request(ctx.files)
+        if not isinstance(audio_bytes, bytes):
+            raise TypeError(
+                f"Expected bytes for audio data, got {type(audio_bytes)}: "
+                f"{audio_bytes}"
+            )
+        if len(audio_bytes) == 0:
+            raise ValueError("Audio data cannot be empty")
+        try:
+            audio_samples = _decode_audio(audio_bytes)
+            if hasattr(audio_samples.data, "numpy"):
+                audio_array = audio_samples.data.numpy()
+            elif hasattr(audio_samples.data, "cpu"):
+                audio_array = audio_samples.data.cpu().numpy()
+            else:
+                audio_array = np.asarray(audio_samples.data)
+            return {
+                "prompt": prompt,
+                "multi_modal_data": {"audio": audio_array},
+            }
+        except (ValueError, TypeError, OSError, RuntimeError) as exc:
+            raise ValueError(
+                f"Failed to decode audio data for vLLM: {exc}"
+            ) from exc
+
+    def _process_streaming_delta(
+        self,
+        request_output: RequestOutput,
+        ctx: _RequestContext,
+        response_handler: VLLMResponseHandler,
+        request_info: RequestInfo,
+        accumulated_text: str,
+        previous_token_count: int,
+        iter_time: float,
+    ) -> tuple[str, int, int]:
+        """Emit one streaming delta; return (accum_text, token_delta, new_prev)."""
+        generated_text = self._text_from_output(request_output)
+        if generated_text == accumulated_text:
+            return (accumulated_text, 0, previous_token_count)
+
+        delta_text = generated_text[len(accumulated_text) :]
+        delta_data = self._openai_payload_for_mode(
+            ctx.mode, delta_text, is_delta=True
+        )
+        if hasattr(request_output, "request_id") and request_output.request_id:
+            delta_data["id"] = request_output.request_id
+        json_str = json.dumps(delta_data)
+        json_str_decoded = (
+            json_str.decode("utf-8") if isinstance(json_str, bytes) else json_str
+        )
+        sse_line = f"data: {json_str_decoded}"
+        iterations = response_handler.add_streaming_line(sse_line)
+
+        total_delta = 0
+        new_prev = previous_token_count
+        if iterations is not None and iterations > 0:
+            out = (
+                request_output.outputs[0]
+                if request_output.outputs
+                else None
+            )
+            if out is not None and out.token_ids is not None:
+                current_count = len(out.token_ids)
+                total_delta = max(0, current_count - previous_token_count)
+                new_prev = current_count
+            else:
+                total_delta = 1
+                new_prev = previous_token_count + 1
+            self._update_token_timing(request_info, iter_time, iterations)
+        return (generated_text, total_delta, new_prev)
+
+    def _raise_generation_error(self, exc: BaseException) -> None:
+        """Re-raise generation failure with context; special case for audio."""
+        error_msg = str(exc)
+        if (
+            "At most 0 audio" in error_msg
+            or "audio(s) may be provided" in error_msg
+        ):
+            raise RuntimeError(
+                f"Generation failed: The model '{self.model}' does not "
+                f"support audio inputs. Use an audio-capable model "
+                f"(e.g. Whisper-based). Original error: {exc}"
+            ) from exc
+        raise RuntimeError(f"Generation failed: {exc}") from exc
+
+    async def _run_generation(
+        self,
+        request: GenerationRequest,
+        request_info: RequestInfo,
+        ctx: _RequestContext,
+        response_handler: VLLMResponseHandler,
+        generate_input: str | dict[str, Any],
+        sampling_params: SamplingParams,
+        request_id: str,
+        state: dict[str, Any],
+    ) -> AsyncIterator[tuple[GenerationResponse, RequestInfo]]:
+        """Run engine.generate loop and yield final (response, request_info)."""
+        state["final_output"] = None
+        state["accumulated_text"] = ""
+        state["total_output_tokens"] = 0
+        previous_token_count = 0
+        stream = ctx.stream
+        engine = self._engine
+        if engine is None:
+            raise RuntimeError("Backend not started up.")
+
+        prompt_arg = cast("Any", generate_input)
+        async for request_output in engine.generate(  # type: ignore[misc]
+            prompt_arg, sampling_params, request_id
+        ):
+            iter_time = time.time()
+            self._update_request_timing(request_info, iter_time)
+            state["final_output"] = request_output
+
+            if (
+                request_output.outputs
+                and len(request_output.outputs) > 0
+                and request_output.outputs[0].finish_reason is not None
+            ):
+                break
+
+            if stream:
+                state["accumulated_text"], token_delta, previous_token_count = (
+                    self._process_streaming_delta(
+                        request_output,
+                        ctx,
+                        response_handler,
+                        request_info,
+                        state["accumulated_text"],
+                        previous_token_count,
+                        iter_time,
+                    )
+                )
+                if token_delta > 0:
+                    state["total_output_tokens"] += token_delta
+
+        request_info.timings.request_end = time.time()
+        result = self._build_final_response(
+            request,
+            request_info,
+            state["final_output"],
+            ctx,
+            response_handler,
+            stream,
+            state["accumulated_text"],
+            state["total_output_tokens"],
+        )
+        if result is not None:
+            yield result
+
+    async def resolve(  # type: ignore[override, misc]
         self,
         request: GenerationRequest,
         request_info: RequestInfo,
@@ -777,17 +976,10 @@ async def resolve(  # type: ignore[override]
         :raises ValueError: If request body/files do not imply a valid mode
         :yields: Single tuple of (response, updated_request_info)
         """
-        # Resolve request context (supports standard pipeline with columns only)
         ctx = self._get_request_context(request)
-
-        # Validate request
         self._validate_backend_initialized()
         self._validate_history(history)
-
-        # Create response handler (no request type; handler infers from response shape)
         response_handler = VLLMResponseHandler()
-
-        # Extract prompt and create sampling parameters
         prompt = self._extract_prompt(ctx.body, ctx.mode)
         sampling_params = self._create_sampling_params(
             ctx.body,
@@ -797,167 +989,45 @@ async def resolve(  # type: ignore[override]
                 else None
             ),
         )
-        stream = ctx.stream
-
-        # For audio requests, construct multimodal input with audio data
+        generate_input: str | dict[str, Any]
         if ctx.mode == "audio":
-            audio_bytes, mimetype = self._extract_audio_from_request(ctx.files)
-
-            # Validate audio_bytes is actually bytes
-            if not isinstance(audio_bytes, bytes):
-                raise TypeError(
-                    f"Expected bytes for audio data, got {type(audio_bytes)}: {audio_bytes}"
-                )
-            if len(audio_bytes) == 0:
-                raise ValueError("Audio data cannot be empty")
-
-            # Decode audio bytes to numpy array for vLLM
-            # vLLM's _get_audio_with_sr expects decoded audio samples (numpy array, torch tensor, etc.)
-            # not raw encoded bytes
-            try:
-                audio_samples = _decode_audio(audio_bytes)
-                # Convert torch tensor to numpy array (vLLM accepts numpy arrays)
-                if hasattr(audio_samples.data, 'numpy'):
-                    audio_array = audio_samples.data.numpy()
-                elif hasattr(audio_samples.data, 'cpu'):
-                    audio_array = audio_samples.data.cpu().numpy()
-                else:
-                    # Already a numpy array or compatible
-                    audio_array = np.asarray(audio_samples.data)
-                
-                # vLLM can accept either:
-                # 1. Just the numpy array (orig_sr will be None, no resampling)
-                # 2. Tuple (array, sample_rate) - but this triggers resampling which needs target_sr
-                # Since the resampler may not have target_sr configured, pass just the array
-                # The model should handle the sample rate appropriately
-                audio_for_vllm = audio_array
-            except Exception as exc:
-                raise ValueError(
-                    f"Failed to decode audio data for vLLM: {exc}"
-                ) from exc
-
-            # Construct multimodal prompt for vLLM v1 transcription models
-            # vLLM v1's generate method accepts multimodal data in this format
-            # The prompt should be a string, and multimodal data passed as a dict
-            # Note: vLLM's _parse_audio_data automatically wraps single arrays in a list
-            prompt_text = prompt if isinstance(prompt, str) else ""
-            
-            prompt = {
-                "prompt": prompt_text,
-                "multi_modal_data": {
-                    "audio": audio_for_vllm,
-                },
-            }
+            prompt_str = prompt if isinstance(prompt, str) else ""
+            generate_input = self._build_audio_generate_input(prompt_str, ctx)
+        else:
+            generate_input = prompt
 
-        # Generate unique request ID for this generation
         request_id = str(uuid.uuid4())
-
         request_info.timings.request_start = time.time()
-        final_output: RequestOutput | None = None
-        accumulated_text = ""
-        total_output_tokens = 0
-        previous_token_count = 0
+        engine = self._engine
+        if engine is None:
+            raise RuntimeError("Backend not started up.")
 
+        gen_state: dict[str, Any] = {}
         try:
-            async for request_output in self._engine.generate(  # type: ignore[misc]
-                prompt, sampling_params, request_id
+            async for result in self._run_generation(
+                request,
+                request_info,
+                ctx,
+                response_handler,
+                generate_input,
+                sampling_params,
+                request_id,
+                gen_state,
             ):
-                # Per-iteration: record timing and keep latest output for final response
-                iter_time = time.time()
-                self._update_request_timing(request_info, iter_time)
-                final_output = request_output
-
-                # Stop when vLLM signals completion (e.g. EOS or max_tokens)
-                if (
-                    request_output.outputs
-                    and len(request_output.outputs) > 0
-                    and request_output.outputs[0].finish_reason is not None
-                ):
-                    break
-
-                # Streaming: emit new text as SSE deltas and accumulate token count
-                if stream:
-                    generated_text = self._text_from_output(request_output)
-
-                    if generated_text != accumulated_text:
-                        # Only the new slice since last iteration (vLLM gives cumulative text)
-                        delta_text = generated_text[len(accumulated_text) :]
-                        # Build OpenAI-style delta: {"text": ...}, choices[].text, or choices[].delta
-                        delta_data = self._openai_payload_for_mode(
-                            ctx.mode, delta_text, is_delta=True
-                        )
-                        if hasattr(request_output, "request_id") and request_output.request_id:
-                            delta_data["id"] = request_output.request_id
-
-                        # Serialize to SSE line and feed to handler (accumulates for compile_streaming)
-                        json_str = json.dumps(delta_data)
-                        if isinstance(json_str, bytes):
-                            json_str = json_str.decode("utf-8")
-                        sse_line = f"data: {json_str}"
-                        iterations = response_handler.add_streaming_line(sse_line)
-
-                        # Update token count (delta from cumulative token_ids) and token timings
-                        if iterations is not None and iterations > 0:
-                            out = (
-                                request_output.outputs[0]
-                                if request_output.outputs
-                                else None
-                            )
-                            # vLLM yields cumulative token_ids; take delta since previous iteration
-                            if out is not None and out.token_ids is not None:
-                                current_count = len(out.token_ids)
-                                chunk_token_count = max(
-                                    0, current_count - previous_token_count
-                                )
-                                previous_token_count = current_count
-                            else:
-                                chunk_token_count = 1
-                                previous_token_count += 1
-                            if chunk_token_count > 0:
-                                total_output_tokens += chunk_token_count
-
-                            self._update_token_timing(request_info, iter_time, iterations)
-
-                        # Track consumed text so next delta is again only the new slice
-                        accumulated_text = generated_text
-
-            request_info.timings.request_end = time.time()
-
-            if (
-                result := self._build_final_response(
-                    request,
-                    request_info,
-                    final_output,
-                    ctx,
-                    response_handler,
-                    stream,
-                    accumulated_text,
-                    total_output_tokens,
-                )
-            ) is not None:
                 yield result
-
         except asyncio.CancelledError as err:
-            if (
-                result := self._build_final_response(
-                    request,
-                    request_info,
-                    final_output,
-                    ctx,
-                    response_handler,
-                    stream,
-                    accumulated_text,
-                    total_output_tokens,
-                )
-            ) is not None:
-                yield result
+            cancel_result = self._build_final_response(
+                request,
+                request_info,
+                gen_state.get("final_output"),
+                ctx,
+                response_handler,
+                ctx.stream,
+                gen_state.get("accumulated_text", ""),
+                gen_state.get("total_output_tokens", 0),
+            )
+            if cancel_result is not None:
+                yield cancel_result
             raise err
-        except Exception as exc:
-            error_msg = str(exc)
-            if "At most 0 audio" in error_msg or "audio(s) may be provided" in error_msg:
-                raise RuntimeError(
-                    f"Generation failed: The model '{self.model}' does not support audio inputs. "
-                    f"For audio transcriptions, please use an audio-capable model (e.g., Whisper-based models). "
-                    f"Original error: {exc}"
-                ) from exc
-            raise RuntimeError(f"Generation failed: {exc}") from exc
+        except (RuntimeError, ValueError, TypeError, OSError, KeyError) as exc:
+            self._raise_generation_error(exc)
diff --git a/src/guidellm/backends/vllm_python/vllm_response.py b/src/guidellm/backends/vllm_python/vllm_response.py
index 2446242b3..624193e65 100644
--- a/src/guidellm/backends/vllm_python/vllm_response.py
+++ b/src/guidellm/backends/vllm_python/vllm_response.py
@@ -81,28 +81,23 @@ def add_streaming_line(self, line: str) -> int | None:
         if "usage" in data:
             self.streaming_usage = data.get("usage") or self.streaming_usage
 
-        # Audio-style: {"text": "..."}
+        result = 0
         if "text" in data and data["text"]:
             self.streaming_texts.append(data["text"])
-            return 1
-
-        # Choices-style
-        choices = data.get("choices", [])
-        if not choices:
-            return 0
-
-        choice = choices[0]
-        # Text completions: {"choices": [{"text": "..."}]}
-        if "text" in choice and choice["text"]:
-            self.streaming_texts.append(choice["text"])
-            return 1
-        # Chat completions: {"choices": [{"delta": {"content": "..."}}]}
-        delta = choice.get("delta", {})
-        if isinstance(delta, dict) and delta.get("content"):
-            self.streaming_texts.append(delta["content"])
-            return 1
-
-        return 0
+            result = 1
+        else:
+            choices = data.get("choices", [])
+            if choices:
+                choice = choices[0]
+                if "text" in choice and choice["text"]:
+                    self.streaming_texts.append(choice["text"])
+                    result = 1
+                else:
+                    delta = choice.get("delta", {})
+                    if isinstance(delta, dict) and delta.get("content"):
+                        self.streaming_texts.append(delta["content"])
+                        result = 1
+        return result
 
     def compile_streaming(self, request: GenerationRequest) -> GenerationResponse:
         """
@@ -112,7 +107,9 @@ def compile_streaming(self, request: GenerationRequest) -> GenerationResponse:
         :return: GenerationResponse with concatenated text and metrics
         """
         text = "".join(self.streaming_texts)
-        input_metrics, output_metrics = self._extract_metrics(self.streaming_usage, text)
+        input_metrics, output_metrics = self._extract_metrics(
+            self.streaming_usage, text
+        )
         request_args = self._get_request_args(request)
 
         return GenerationResponse(
@@ -132,11 +129,11 @@ def _extract_text_and_usage(
 
         if "text" in response_dict:
             text = response_dict["text"]
-            return (text if isinstance(text, str) else str(text), usage)
+            return text if isinstance(text, str) else str(text), usage
 
         choices = response_dict.get("choices", [])
         if not choices:
-            return ("", usage)
+            return "", usage
 
         choice = choices[0]
         if "text" in choice:
@@ -147,7 +144,7 @@ def _extract_text_and_usage(
         else:
             text = ""
 
-        return (text if isinstance(text, str) else str(text), usage)
+        return text if isinstance(text, str) else str(text), usage
 
     def _extract_metrics(
         self, usage: dict[str, Any] | None, text: str
@@ -203,7 +200,7 @@ def _parse_line(self, line: str) -> dict[str, Any] | None:
         line = line[len("data:") :].strip()
         try:
             return json.loads(line)
-        except Exception:
+        except (ValueError, TypeError):
             return {}
 
     def _get_request_args(self, request: GenerationRequest) -> str | None:

From 774bcd86e4a1e6261851fc4ff4d239cf484aaa6e Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 16 Feb 2026 12:58:29 -0500
Subject: [PATCH 47/56] Added tests for vllm_pthon backend.

Generated-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 tests/unit/backends/test_backend.py           |   17 +
 tests/unit/backends/vllm_python/__init__.py   |    0
 tests/unit/backends/vllm_python/test_vllm.py  | 1469 +++++++++++++++++
 .../vllm_python/test_vllm_response.py         |  225 +++
 4 files changed, 1711 insertions(+)
 create mode 100644 tests/unit/backends/vllm_python/__init__.py
 create mode 100644 tests/unit/backends/vllm_python/test_vllm.py
 create mode 100644 tests/unit/backends/vllm_python/test_vllm_response.py

diff --git a/tests/unit/backends/test_backend.py b/tests/unit/backends/test_backend.py
index b7b5ed0bf..8157bac27 100644
--- a/tests/unit/backends/test_backend.py
+++ b/tests/unit/backends/test_backend.py
@@ -303,6 +303,23 @@ def test_openai_backend_registered(self):
         assert isinstance(backend, OpenAIHTTPBackend)
         assert backend.type_ == "openai_http"
 
+    @pytest.mark.smoke
+    def test_vllm_python_backend_registered(self):
+        """
+        Test that vllm_python backend is registered and createable.
+        ## WRITTEN BY AI ##
+        """
+        from unittest.mock import patch
+
+        from guidellm.backends.vllm_python.vllm import VLLMPythonBackend
+
+        assert Backend.is_registered("vllm_python")
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend = Backend.create("vllm_python", model="test-model")
+        assert isinstance(backend, VLLMPythonBackend)
+        assert backend.model == "test-model"
+        assert backend.type_ == "vllm_python"
+
     @pytest.mark.smoke
     def test_backend_registry_functionality(self):
         """Test that backend registry functions work."""
diff --git a/tests/unit/backends/vllm_python/__init__.py b/tests/unit/backends/vllm_python/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/unit/backends/vllm_python/test_vllm.py b/tests/unit/backends/vllm_python/test_vllm.py
new file mode 100644
index 000000000..9a75223f6
--- /dev/null
+++ b/tests/unit/backends/vllm_python/test_vllm.py
@@ -0,0 +1,1469 @@
+"""
+Unit tests for VLLM Python backend.
+
+Tests _get_request_context (mode from body/files; body from columns or arguments)
+and request_format (plain, default-template, custom template) for chat prompts.
+"""
+
+from __future__ import annotations
+
+import asyncio
+from unittest.mock import Mock, patch
+
+import numpy as np
+import pytest
+
+from guidellm.backends.vllm_python.vllm import VLLMPythonBackend, _RequestContext
+from guidellm.schemas import (
+    GenerationRequest,
+    GenerationResponse,
+    RequestInfo,
+    UsageMetrics,
+)
+
+
+@pytest.fixture
+def backend():
+    """VLLMPythonBackend instance without requiring vllm to be installed."""
+    with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+        return VLLMPythonBackend(model="test-model")
+
+
+class TestVLLMGetRequestContext:
+    """
+    Test _get_request_context: mode inferred from body and files.
+    ## WRITTEN BY AI ##
+    """
+
+    @pytest.mark.smoke
+    def test_columns_only_infers_chat(self, backend):
+        """
+        Request with only columns (no arguments) builds messages and infers mode chat.
+        ## WRITTEN BY AI ##
+        """
+        request = GenerationRequest(columns={"text_column": ["hello"]})
+        ctx = backend._get_request_context(request)
+        assert ctx.mode == "chat"
+        assert "messages" in ctx.body
+        assert len(ctx.body["messages"]) == 1
+        assert ctx.body["messages"][0]["role"] == "user"
+        assert ctx.stream is True  # backend default stream=True (match HTTP behavior)
+        assert ctx.files == {}
+
+    @pytest.mark.smoke
+    def test_columns_only_uses_backend_stream_false(self):
+        """
+        When backend.stream=False and no arguments, ctx.stream is False.
+        ## WRITTEN BY AI ##
+        """
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend = VLLMPythonBackend(model="test-model", stream=False)
+        request = GenerationRequest(columns={"text_column": ["hello"]})
+        ctx = backend._get_request_context(request)
+        assert ctx.stream is False
+
+    @pytest.mark.smoke
+    def test_arguments_stream_overrides_backend_stream(self):
+        """
+        When request.arguments.stream is set, it overrides backend.stream.
+        ## WRITTEN BY AI ##
+        """
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend = VLLMPythonBackend(model="test-model", stream=False)
+        request = Mock(spec=GenerationRequest)
+        request.arguments = Mock()
+        request.arguments.body = {"messages": [{"role": "user", "content": "Hi"}]}
+        request.arguments.stream = True  # override backend's stream=False
+        request.arguments.files = {}
+        ctx = backend._get_request_context(request)
+        assert ctx.stream is True
+
+    @pytest.mark.smoke
+    def test_arguments_body_prompt_infers_text(self, backend):
+        """
+        Request with arguments.body containing only prompt infers mode text.
+        ## WRITTEN BY AI ##
+        """
+        request = Mock(spec=GenerationRequest)
+        request.arguments = Mock()
+        request.arguments.body = {"prompt": "Complete this"}
+        request.arguments.stream = False
+        request.arguments.files = {}
+        ctx = backend._get_request_context(request)
+        assert ctx.mode == "text"
+        assert ctx.body == {"prompt": "Complete this"}
+        assert ctx.files == {}
+
+    @pytest.mark.smoke
+    def test_arguments_body_messages_infers_chat(self, backend):
+        """
+        Request with arguments.body containing messages infers mode chat.
+        ## WRITTEN BY AI ##
+        """
+        request = Mock(spec=GenerationRequest)
+        request.arguments = Mock()
+        request.arguments.body = {
+            "messages": [{"role": "user", "content": "Hi"}],
+        }
+        request.arguments.stream = True
+        request.arguments.files = {}
+        ctx = backend._get_request_context(request)
+        assert ctx.mode == "chat"
+        assert ctx.body["messages"] == [{"role": "user", "content": "Hi"}]
+        assert ctx.stream is True
+        assert ctx.files == {}
+
+    @pytest.mark.smoke
+    def test_arguments_non_empty_files_infers_audio(self, backend):
+        """
+        Request with non-empty files infers mode audio.
+        ## WRITTEN BY AI ##
+        """
+        request = Mock(spec=GenerationRequest)
+        request.arguments = Mock()
+        request.arguments.body = {}
+        request.arguments.stream = False
+        request.arguments.files = {"file": (b"audio-data", "audio/wav")}
+        ctx = backend._get_request_context(request)
+        assert ctx.mode == "audio"
+        assert ctx.files == {"file": (b"audio-data", "audio/wav")}
+
+    @pytest.mark.smoke
+    def test_arguments_files_take_precedence_over_body(self, backend):
+        """
+        When files is non-empty, mode is audio even if body has messages.
+        ## WRITTEN BY AI ##
+        """
+        request = Mock(spec=GenerationRequest)
+        request.arguments = Mock()
+        request.arguments.body = {"messages": [{"role": "user", "content": "Hi"}]}
+        request.arguments.stream = False
+        request.arguments.files = {"file": (b"x", "audio/wav")}
+        ctx = backend._get_request_context(request)
+        assert ctx.mode == "audio"
+
+    @pytest.mark.smoke
+    def test_arguments_empty_body_no_files_raises(self, backend):
+        """
+        Request with no prompt, no messages, and no files raises ValueError.
+        ## WRITTEN BY AI ##
+        """
+        request = Mock(spec=GenerationRequest)
+        request.arguments = Mock()
+        request.arguments.body = {}
+        request.arguments.stream = False
+        request.arguments.files = {}
+        with pytest.raises(
+            ValueError, match="Request must include prompt, messages, or audio files"
+        ):
+            backend._get_request_context(request)
+
+    @pytest.mark.smoke
+    def test_arguments_body_prompt_key_empty_string_infers_text(self, backend):
+        """
+        Body with key 'prompt' present (even empty string) infers mode text.
+        ## WRITTEN BY AI ##
+        """
+        request = Mock(spec=GenerationRequest)
+        request.arguments = Mock()
+        request.arguments.body = {"prompt": ""}
+        request.arguments.stream = False
+        request.arguments.files = {}
+        ctx = backend._get_request_context(request)
+        assert ctx.mode == "text"
+        assert ctx.body["prompt"] == ""
+
+    @pytest.mark.smoke
+    def test_columns_prefix_and_text_build_messages(self, backend):
+        """
+        Columns with prefix_column and text_column build multiple messages, mode chat.
+        ## WRITTEN BY AI ##
+        """
+        request = GenerationRequest(
+            columns={
+                "prefix_column": ["System prompt"],
+                "text_column": ["User question"],
+            }
+        )
+        ctx = backend._get_request_context(request)
+        assert ctx.mode == "chat"
+        assert len(ctx.body["messages"]) == 2
+        assert ctx.body["messages"][0]["role"] == "system"
+        assert ctx.body["messages"][0]["content"] == "System prompt"
+        assert ctx.body["messages"][1]["role"] == "user"
+        assert ctx.body["messages"][1]["content"][0]["text"] == "User question"
+
+
+class TestVLLMRequestFormat:
+    """
+    Test request_format: plain, default-template, and custom template.
+    """
+
+    @pytest.mark.smoke
+    def test_request_format_plain_produces_concatenated_prompt(self, backend):
+        """
+        With request_format=plain, chat prompt is plain concatenation (no template).
+        ## WRITTEN BY AI ##
+        """
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend_plain = VLLMPythonBackend(
+                model="test-model", request_format="plain"
+            )
+        body = {
+            "messages": [
+                {"role": "user", "content": "Hello"},
+                {"role": "assistant", "content": "Hi"},
+                {"role": "user", "content": "Bye"},
+            ]
+        }
+        # _extract_prompt for chat uses self._engine.tokenizer only when not plain
+        prompt = backend_plain._extract_prompt(body, "chat")
+        assert "User: Hello" in prompt
+        assert "Assistant: Hi" in prompt
+        assert "User: Bye" in prompt
+        assert "Assistant: " in prompt
+        # No template markers like [INST] or <|im_start|>
+        assert "[INST]" not in prompt
+        assert "<|im_start|>" not in prompt
+
+    @pytest.mark.smoke
+    def test_request_format_default_template_uses_apply_chat_template(self, backend):
+        """
+        With request_format=default-template or None, apply_chat_template is used.
+        ## WRITTEN BY AI ##
+        """
+        body = {"messages": [{"role": "user", "content": "Hi"}]}
+        mock_tokenizer = Mock()
+        mock_tokenizer.apply_chat_template.return_value = "formatted_prompt"
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend_default = VLLMPythonBackend(
+                model="test-model", request_format="default-template"
+            )
+            backend_default._engine = Mock()
+            backend_default._engine.tokenizer = mock_tokenizer
+        prompt = backend_default._extract_prompt(body, "chat")
+        assert prompt == "formatted_prompt"
+        mock_tokenizer.apply_chat_template.assert_called_once()
+        call_kw = mock_tokenizer.apply_chat_template.call_args[1]
+        assert call_kw.get("tokenize") is False
+        assert call_kw.get("add_generation_prompt") is True
+
+    @pytest.mark.smoke
+    def test_request_format_none_uses_apply_chat_template(self, backend):
+        """
+        With request_format=None, tokenizer.apply_chat_template is used.
+        ## WRITTEN BY AI ##
+        """
+        body = {"messages": [{"role": "user", "content": "Hi"}]}
+        mock_tokenizer = Mock()
+        mock_tokenizer.apply_chat_template.return_value = "default_prompt"
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend_none = VLLMPythonBackend(model="test-model")
+            backend_none._engine = Mock()
+            backend_none._engine.tokenizer = mock_tokenizer
+        prompt = backend_none._extract_prompt(body, "chat")
+        assert prompt == "default_prompt"
+        mock_tokenizer.apply_chat_template.assert_called_once()
+
+    @pytest.mark.smoke
+    def test_request_format_custom_template_string_sets_tokenizer_and_applies(
+        self, backend
+    ):
+        """
+        With request_format=custom, chat_template is set then apply_chat_template.
+        ## WRITTEN BY AI ##
+        """
+        body = {"messages": [{"role": "user", "content": "Hi"}]}
+        mock_tokenizer = Mock()
+        mock_tokenizer.apply_chat_template.return_value = "custom_prompt"
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend_custom = VLLMPythonBackend(
+                model="test-model",
+                request_format="{{ messages[0]['content'] }}",
+            )
+            backend_custom._engine = Mock()
+            backend_custom._engine.tokenizer = mock_tokenizer
+        prompt = backend_custom._extract_prompt(body, "chat")
+        assert prompt == "custom_prompt"
+        assert mock_tokenizer.chat_template == "{{ messages[0]['content'] }}"
+        mock_tokenizer.apply_chat_template.assert_called_once()
+
+    @pytest.mark.smoke
+    def test_request_format_custom_template_from_file_sets_tokenizer_and_applies(
+        self, backend, tmp_path
+    ):
+        """
+        With request_format=file path, chat_template is set from file then applied.
+        ## WRITTEN BY AI ##
+        """
+        template_file = tmp_path / "template.jinja"
+        template_file.write_text("Custom: {{ messages[0]['content'] }}")
+        body = {"messages": [{"role": "user", "content": "Hi"}]}
+        mock_tokenizer = Mock()
+        mock_tokenizer.apply_chat_template.return_value = "Custom: Hi"
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend_file = VLLMPythonBackend(
+                model="test-model", request_format=str(template_file)
+            )
+            backend_file._engine = Mock()
+            backend_file._engine.tokenizer = mock_tokenizer
+        prompt = backend_file._extract_prompt(body, "chat")
+        assert prompt == "Custom: Hi"
+        assert mock_tokenizer.chat_template == "Custom: {{ messages[0]['content'] }}"
+        mock_tokenizer.apply_chat_template.assert_called_once()
+
+    @pytest.mark.smoke
+    def test_request_format_stored_and_passed_to_vllm_config(self, backend):
+        """
+        Custom request_format is added to vllm_config as chat_template.
+        ## WRITTEN BY AI ##
+        """
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend_custom = VLLMPythonBackend(
+                model="test-model",
+                request_format="/path/to/template.jinja",
+            )
+        assert backend_custom.request_format == "/path/to/template.jinja"
+        assert (
+            backend_custom.vllm_config.get("chat_template") == "/path/to/template.jinja"
+        )
+
+    @pytest.mark.smoke
+    def test_request_format_plain_not_in_vllm_config(self, backend):
+        """
+        request_format=plain does not add chat_template to vllm_config.
+        ## WRITTEN BY AI ##
+        """
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend_plain = VLLMPythonBackend(
+                model="test-model", request_format="plain"
+            )
+        assert backend_plain.request_format == "plain"
+        assert "chat_template" not in backend_plain.vllm_config
+
+    @pytest.mark.smoke
+    def test_request_format_default_template_not_in_vllm_config(self, backend):
+        """
+        request_format=default-template does not add chat_template to vllm_config.
+        ## WRITTEN BY AI ##
+        """
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend_def = VLLMPythonBackend(
+                model="test-model", request_format="default-template"
+            )
+        assert backend_def.request_format == "default-template"
+        assert "chat_template" not in backend_def.vllm_config
+
+    @pytest.mark.smoke
+    def test_vllm_config_empty_uses_vllm_defaults(self):
+        """
+        With vllm_config empty or None, backend only sets model; no extra keys.
+        ## WRITTEN BY AI ##
+        """
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend_empty = VLLMPythonBackend(model="test-model", vllm_config={})
+            backend_none = VLLMPythonBackend(model="test-model", vllm_config=None)
+        for b in (backend_empty, backend_none):
+            assert b.vllm_config.get("model") == "test-model"
+            assert "tensor_parallel_size" not in b.vllm_config
+            assert "gpu_memory_utilization" not in b.vllm_config
+
+
+class TestVLLMExtractPromptTokenizerWarnings:
+    """
+    Test warnings when tokenizer is set but not used for prompt extraction.
+    """
+
+    @pytest.mark.smoke
+    def test_text_mode_warns_when_engine_has_tokenizer(self, backend):
+        """
+        With mode=text and engine.tokenizer set, a warning is logged.
+        ## WRITTEN BY AI ##
+        """
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend._engine = Mock()
+            backend._engine.tokenizer = Mock()
+        with patch("guidellm.backends.vllm_python.vllm.logger") as mock_logger:
+            result = backend._extract_prompt({"prompt": "Complete this"}, "text")
+        assert result == "Complete this"
+        mock_logger.warning.assert_called_once()
+        call_msg = mock_logger.warning.call_args[0][0]
+        assert "Tokenizer is set" in call_msg
+        assert "mode was inferred as text" in call_msg
+
+    @pytest.mark.smoke
+    def test_text_mode_no_warning_when_engine_has_no_tokenizer(self, backend):
+        """
+        With mode=text and no engine tokenizer, no warning is logged.
+        ## WRITTEN BY AI ##
+        """
+        backend._engine = None
+        with patch("guidellm.backends.vllm_python.vllm.logger") as mock_logger:
+            result = backend._extract_prompt({"prompt": "Complete this"}, "text")
+        assert result == "Complete this"
+        mock_logger.warning.assert_not_called()
+
+    @pytest.mark.smoke
+    def test_chat_plain_no_warning_when_engine_has_no_tokenizer(self, backend):
+        """
+        With request_format=plain and no engine tokenizer, no warning is logged.
+        ## WRITTEN BY AI ##
+        """
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend_plain = VLLMPythonBackend(
+                model="test-model", request_format="plain"
+            )
+            backend_plain._engine = None
+        body = {"messages": [{"role": "user", "content": "Hi"}]}
+        with patch("guidellm.backends.vllm_python.vllm.logger") as mock_logger:
+            backend_plain._extract_prompt(body, "chat")
+        mock_logger.warning.assert_not_called()
+
+
+class TestVLLMStreamingUsageFromOutput:
+    """
+    Test _usage_from_output (stream path): token_ids vs tokenizer fallback.
+    """
+
+    @pytest.mark.smoke
+    def test_streaming_usage_from_output_token_ids_none_uses_tokenizer(self, backend):
+        """
+        When token_ids is None, output_tokens are derived from tokenizer.encode.
+        ## WRITTEN BY AI ##
+        """
+        mock_out = Mock()
+        mock_out.token_ids = None
+        mock_out.text = "Hello world"
+        mock_final = Mock()
+        mock_final.prompt_token_ids = [1, 2, 3]
+        mock_final.outputs = [mock_out]
+        mock_info = Mock()
+        mock_info.timings.token_iterations = 10
+        mock_tokenizer = Mock()
+        mock_tokenizer.encode.return_value = [10, 20, 30, 40, 50]  # 5 tokens
+        backend._engine = Mock()
+        backend._engine.tokenizer = mock_tokenizer
+        usage = backend._usage_from_output(
+            mock_final,
+            accumulated_text="Hello world",
+            request_info=mock_info,
+        )
+        assert usage is not None
+        assert usage["prompt_tokens"] == 3
+        assert usage["completion_tokens"] == 5
+        assert usage["total_tokens"] == 8
+        mock_tokenizer.encode.assert_called_once_with(
+            "Hello world", add_special_tokens=False
+        )
+
+    @pytest.mark.smoke
+    def test_streaming_usage_from_output_uses_token_ids_when_available(self, backend):
+        """
+        When token_ids is set, it is used and tokenizer is not called.
+        ## WRITTEN BY AI ##
+        """
+        mock_out = Mock()
+        mock_out.token_ids = [100, 101, 102, 103]
+        mock_out.text = "Hi"
+        mock_final = Mock()
+        mock_final.prompt_token_ids = [1, 2]
+        mock_final.outputs = [mock_out]
+        mock_info = Mock()
+        mock_info.timings.token_iterations = 1
+        backend._engine = Mock()
+        backend._engine.tokenizer = Mock()
+        usage = backend._usage_from_output(
+            mock_final,
+            accumulated_text="Hi",
+            request_info=mock_info,
+        )
+        assert usage is not None
+        assert usage["prompt_tokens"] == 2
+        assert usage["completion_tokens"] == 4
+        assert usage["total_tokens"] == 6
+        backend._engine.tokenizer.encode.assert_not_called()
+
+    @pytest.mark.smoke
+    def test_streaming_usage_from_output_fallback_to_token_iterations(self, backend):
+        """
+        When token_ids is None and tokenizer fails, use token_iterations.
+        ## WRITTEN BY AI ##
+        """
+        mock_out = Mock()
+        mock_out.token_ids = None
+        mock_out.text = "Hi"
+        mock_final = Mock()
+        mock_final.prompt_token_ids = [1]
+        mock_final.outputs = [mock_out]
+        mock_info = Mock()
+        mock_info.timings.token_iterations = 7
+        backend._engine = Mock()
+        backend._engine.tokenizer = Mock(side_effect=RuntimeError("no tokenizer"))
+        usage = backend._usage_from_output(
+            mock_final,
+            accumulated_text="Hi",
+            request_info=mock_info,
+        )
+        assert usage is not None
+        assert usage["completion_tokens"] == 7
+        assert usage["total_tokens"] == 8
+
+
+class TestVLLMStreamingCumulativeTokenCount:
+    """
+    Test that streaming correctly counts tokens when vLLM yields CUMULATIVE token_ids.
+    """
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_cumulative_token_ids_counted_as_delta(self, backend):
+        """
+        vLLM yields cumulative token_ids; we add delta only.
+        ## WRITTEN BY AI ##
+        """
+        # Simulate 3 chunks, cumulative token_ids [1],[1,2],[1,2,3] -> 3 tokens
+        out1 = Mock()
+        out1.text = "A"
+        out1.token_ids = [100]
+        out1.finish_reason = None
+        out2 = Mock()
+        out2.text = "AB"
+        out2.token_ids = [100, 101]
+        out2.finish_reason = None
+        out3 = Mock()
+        out3.text = "ABC"
+        out3.token_ids = [100, 101, 102]
+        out3.finish_reason = None
+        req1, req2, req3 = Mock(), Mock(), Mock()
+        for req in (req1, req2, req3):
+            req.request_id = None
+            req.prompt_token_ids = [1, 2]  # for _usage_from_output
+        req1.outputs, req2.outputs, req3.outputs = [out1], [out2], [out3]
+
+        async def mock_generate(_prompt, _params, _req_id):
+            for r in (req1, req2, req3):
+                yield r
+
+        backend._engine = Mock()
+        backend._engine.generate = mock_generate
+        request = GenerationRequest(
+            columns={"text_column": ["Hi"]},
+            output_metrics=UsageMetrics(text_tokens=10),
+        )
+        request_info = RequestInfo()
+
+        with patch.object(backend, "_extract_prompt", return_value="Hi"):
+            final_response = None
+            async for response, _ in backend.resolve(request, request_info):
+                final_response = response
+
+        assert final_response is not None
+        assert final_response.output_metrics.text_tokens == 3
+
+
+class TestVLLMCreateSamplingParams:
+    """
+    Test _create_sampling_params: max_tokens from body vs request override.
+    """
+
+    @pytest.mark.smoke
+    def test_max_tokens_override_used_when_body_has_no_max_tokens(self, backend):
+        """
+        When body has no max_tokens, max_tokens_override is used.
+        ## WRITTEN BY AI ##
+        """
+        body = {"messages": [{"role": "user", "content": "Hi"}]}
+        params = backend._create_sampling_params(body, max_tokens_override=2000)
+        assert params.max_tokens == 2000
+
+    @pytest.mark.smoke
+    def test_body_max_tokens_takes_precedence_over_override(self, backend):
+        """
+        When body has max_tokens, override is ignored.
+        ## WRITTEN BY AI ##
+        """
+        body = {"messages": [{"role": "user", "content": "Hi"}], "max_tokens": 64}
+        params = backend._create_sampling_params(body, max_tokens_override=2000)
+        assert params.max_tokens == 64
+
+    @pytest.mark.smoke
+    def test_default_max_tokens_when_no_body_no_override(self, backend):
+        """
+        When body has no max_tokens and no override, default is 16.
+        ## WRITTEN BY AI ##
+        """
+        body = {"messages": [{"role": "user", "content": "Hi"}]}
+        params = backend._create_sampling_params(body)
+        assert params.max_tokens == 16
+
+    @pytest.mark.smoke
+    def test_override_used_sets_ignore_eos_and_stop_like_http(self, backend):
+        """
+        When max_tokens_override is used, ignore_eos=True and stop=[] to match HTTP.
+        ## WRITTEN BY AI ##
+        """
+        body = {"messages": [{"role": "user", "content": "Hi"}]}
+        params = backend._create_sampling_params(body, max_tokens_override=2000)
+        assert params.max_tokens == 2000
+        assert params.ignore_eos is True
+        assert params.stop == []
+
+    @pytest.mark.smoke
+    def test_override_used_with_600_also_sets_ignore_eos_and_stop(self, backend):
+        """
+        When max_tokens_override is used (e.g. 600), same: ignore_eos=True, stop=[].
+        ## WRITTEN BY AI ##
+        """
+        body = {"messages": [{"role": "user", "content": "Hi"}]}
+        params = backend._create_sampling_params(body, max_tokens_override=600)
+        assert params.max_tokens == 600
+        assert params.ignore_eos is True
+        assert params.stop == []
+
+    @pytest.mark.smoke
+    def test_body_max_tokens_uses_body_ignore_eos_and_stop(self, backend):
+        """
+        When body has max_tokens, ignore_eos and stop come from body.
+        ## WRITTEN BY AI ##
+        """
+        body = {
+            "messages": [{"role": "user", "content": "Hi"}],
+            "max_tokens": 64,
+            "ignore_eos": True,
+            "stop": ["</s>"],
+        }
+        params = backend._create_sampling_params(body, max_tokens_override=2000)
+        assert params.max_tokens == 64
+        assert params.ignore_eos is True
+        assert params.stop == ["</s>"]
+
+    @pytest.mark.smoke
+    def test_max_tokens_zero_safeguard(self, backend):
+        """
+        When body has max_tokens=0, we use 16 so vLLM never receives 0.
+        ## WRITTEN BY AI ##
+        """
+        body = {"messages": [{"role": "user", "content": "Hi"}], "max_tokens": 0}
+        params = backend._create_sampling_params(body)
+        assert params.max_tokens == 16
+
+
+class TestVLLMLifecycle:
+    """
+    process_startup/shutdown/validate with mocked AsyncEngineArgs and AsyncLLMEngine.
+    """
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_process_startup_success(self):
+        """
+        Success: mock AsyncEngineArgs and from_engine_args; set _engine, _in_process.
+        ## WRITTEN BY AI ##
+        """
+        mock_engine = Mock()
+        with (
+            patch("guidellm.backends.vllm_python.vllm._check_vllm_available"),
+            patch(
+                "guidellm.backends.vllm_python.vllm.AsyncEngineArgs",
+                return_value=Mock(),
+            ),
+            patch(
+                "guidellm.backends.vllm_python.vllm.AsyncLLMEngine"
+            ) as mock_engine_cls,
+        ):
+            mock_engine_cls.from_engine_args = Mock(return_value=mock_engine)
+            backend = VLLMPythonBackend(model="test-model")
+            await backend.process_startup()
+        assert backend._engine is mock_engine
+        assert backend._in_process is True
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_process_startup_idempotency_raises(self):
+        """
+        Idempotency: raise RuntimeError when _in_process already True.
+        ## WRITTEN BY AI ##
+        """
+        mock_engine = Mock()
+        with (
+            patch("guidellm.backends.vllm_python.vllm._check_vllm_available"),
+            patch(
+                "guidellm.backends.vllm_python.vllm.AsyncEngineArgs",
+                return_value=Mock(),
+            ),
+            patch(
+                "guidellm.backends.vllm_python.vllm.AsyncLLMEngine"
+            ) as mock_engine_cls,
+        ):
+            mock_engine_cls.from_engine_args = Mock(return_value=mock_engine)
+            backend = VLLMPythonBackend(model="test-model")
+            await backend.process_startup()
+            with pytest.raises(RuntimeError, match="Backend already started up"):
+                await backend.process_startup()
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_process_shutdown_success(self):
+        """
+        Success: _engine.shutdown() called; _engine None, _in_process False.
+        ## WRITTEN BY AI ##
+        """
+        mock_engine = Mock()
+        with (
+            patch("guidellm.backends.vllm_python.vllm._check_vllm_available"),
+            patch(
+                "guidellm.backends.vllm_python.vllm.AsyncEngineArgs",
+                return_value=Mock(),
+            ),
+            patch(
+                "guidellm.backends.vllm_python.vllm.AsyncLLMEngine"
+            ) as mock_engine_cls,
+        ):
+            mock_engine_cls.from_engine_args = Mock(return_value=mock_engine)
+            backend = VLLMPythonBackend(model="test-model")
+            await backend.process_startup()
+            await backend.process_shutdown()
+        mock_engine.shutdown.assert_called_once()
+        assert backend._engine is None
+        assert backend._in_process is False
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_process_shutdown_not_started_raises(self):
+        """
+        Raise RuntimeError when not started.
+        ## WRITTEN BY AI ##
+        """
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend = VLLMPythonBackend(model="test-model")
+            backend._in_process = False
+            backend._engine = None
+        with pytest.raises(RuntimeError, match="Backend not started up"):
+            await backend.process_shutdown()
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_validate_engine_none_raises(self, backend):
+        """
+        Raise RuntimeError when _engine is None.
+        ## WRITTEN BY AI ##
+        """
+        backend._engine = None
+        with pytest.raises(RuntimeError, match="Backend not started up"):
+            await backend.validate()
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_validate_success(self, backend):
+        """
+        Success: mock _engine.generate async iterator yielding one item; no raise.
+        ## WRITTEN BY AI ##
+        """
+
+        async def one_yield(*args, **kwargs):
+            yield Mock(outputs=[Mock()], prompt_token_ids=[])
+
+        backend._engine = Mock()
+        backend._engine.generate = one_yield
+        with patch(
+            "guidellm.backends.vllm_python.vllm.SamplingParams", return_value=Mock()
+        ):
+            await backend.validate()
+
+
+class TestVLLMModels:
+    """
+    available_models and default_model.
+    """
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_available_models_returns_model_list(self, backend):
+        """
+        Returns [self.model].
+        ## WRITTEN BY AI ##
+        """
+        models = await backend.available_models()
+        assert models == ["test-model"]
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_default_model_returns_model(self, backend):
+        """
+        Returns self.model.
+        ## WRITTEN BY AI ##
+        """
+        model = await backend.default_model()
+        assert model == "test-model"
+
+
+class TestVLLMValidateHelpers:
+    """
+    _validate_backend_initialized and _validate_history.
+    """
+
+    @pytest.mark.smoke
+    def test_validate_backend_initialized_raises_when_engine_none(self, backend):
+        """
+        Raises RuntimeError when _engine is None.
+        ## WRITTEN BY AI ##
+        """
+        backend._engine = None
+        with pytest.raises(RuntimeError, match="Backend not started up"):
+            backend._validate_backend_initialized()
+
+    @pytest.mark.smoke
+    def test_validate_backend_initialized_passes_when_engine_set(self, backend):
+        """
+        Does not raise when _engine is set.
+        ## WRITTEN BY AI ##
+        """
+        backend._engine = Mock()
+        backend._validate_backend_initialized()
+
+    @pytest.mark.smoke
+    def test_validate_history_raises_when_history_not_none(self, backend):
+        """
+        Raises NotImplementedError when history is not None.
+        ## WRITTEN BY AI ##
+        """
+        with pytest.raises(
+            NotImplementedError, match="Multi-turn requests not yet supported"
+        ):
+            backend._validate_history([(Mock(), Mock())])
+
+    @pytest.mark.smoke
+    def test_validate_history_passes_when_history_none(self, backend):
+        """
+        Does not raise when history is None.
+        ## WRITTEN BY AI ##
+        """
+        backend._validate_history(None)
+
+
+class TestVLLMTextFromOutput:
+    """
+    _text_from_output: None, empty outputs, normal.
+    """
+
+    @pytest.mark.smoke
+    def test_text_from_output_none_returns_empty(self, backend):
+        """
+        output is None -> "".
+        ## WRITTEN BY AI ##
+        """
+        assert backend._text_from_output(None) == ""
+
+    @pytest.mark.smoke
+    def test_text_from_output_empty_outputs_returns_empty(self, backend):
+        """
+        output.outputs empty -> "".
+        ## WRITTEN BY AI ##
+        """
+        mock_out = Mock()
+        mock_out.outputs = []
+        assert backend._text_from_output(mock_out) == ""
+
+    @pytest.mark.smoke
+    def test_text_from_output_normal_returns_first_text(self, backend):
+        """
+        Normal -> output.outputs[0].text.
+        ## WRITTEN BY AI ##
+        """
+        mock_out = Mock()
+        mock_out.outputs = [Mock(text="Hello world")]
+        assert backend._text_from_output(mock_out) == "Hello world"
+
+    @pytest.mark.smoke
+    def test_text_from_output_none_text_returns_empty(self, backend):
+        """
+        output.outputs[0].text is None -> "".
+        ## WRITTEN BY AI ##
+        """
+        mock_out = Mock()
+        mock_out.outputs = [Mock(text=None)]
+        assert backend._text_from_output(mock_out) == ""
+
+
+class TestVLLMOpenAIPayloadForMode:
+    """
+    _openai_payload_for_mode: audio, text, chat delta/message.
+    """
+
+    @pytest.mark.smoke
+    def test_openai_payload_audio_returns_text_key(self, backend):
+        """
+        mode audio -> {"text": ...}.
+        ## WRITTEN BY AI ##
+        """
+        out = backend._openai_payload_for_mode("audio", "transcribed", is_delta=False)
+        assert out == {"text": "transcribed"}
+
+    @pytest.mark.smoke
+    def test_openai_payload_text_returns_choices_text(self, backend):
+        """
+        mode text -> choices[].text.
+        ## WRITTEN BY AI ##
+        """
+        out = backend._openai_payload_for_mode("text", "completion", is_delta=False)
+        assert out == {"choices": [{"text": "completion"}]}
+
+    @pytest.mark.smoke
+    def test_openai_payload_chat_delta_returns_choices_delta(self, backend):
+        """
+        mode chat is_delta=True -> choices[].delta.
+        ## WRITTEN BY AI ##
+        """
+        out = backend._openai_payload_for_mode("chat", "hi", is_delta=True)
+        assert out == {"choices": [{"delta": {"content": "hi", "role": "assistant"}}]}
+
+    @pytest.mark.smoke
+    def test_openai_payload_chat_message_returns_choices_message(self, backend):
+        """
+        mode chat is_delta=False -> choices[].message.
+        ## WRITTEN BY AI ##
+        """
+        out = backend._openai_payload_for_mode("chat", "hello", is_delta=False)
+        assert out == {
+            "choices": [{"message": {"content": "hello", "role": "assistant"}}]
+        }
+
+
+class TestVLLMUsageFromOutputNonStream:
+    """
+    _usage_from_output with request_info=None (non-stream path).
+    """
+
+    @pytest.mark.smoke
+    def test_usage_from_output_non_stream_uses_output_token_counts(self, backend):
+        """
+        Non-stream: use only output token counts (no tokenizer/request_info).
+        ## WRITTEN BY AI ##
+        """
+        mock_out = Mock()
+        mock_out.prompt_token_ids = [1, 2, 3]
+        mock_out.outputs = [Mock(token_ids=[10, 20])]
+        usage = backend._usage_from_output(mock_out, request_info=None)
+        assert usage == {
+            "prompt_tokens": 3,
+            "completion_tokens": 2,
+            "total_tokens": 5,
+        }
+
+    @pytest.mark.smoke
+    def test_usage_from_output_non_stream_outputs_empty(self, backend):
+        """
+        Non-stream with no outputs -> completion_tokens 0.
+        ## WRITTEN BY AI ##
+        """
+        mock_out = Mock()
+        mock_out.prompt_token_ids = [1]
+        mock_out.outputs = []
+        usage = backend._usage_from_output(mock_out, request_info=None)
+        assert usage["completion_tokens"] == 0
+        assert usage["prompt_tokens"] == 1
+
+
+class TestVLLMBuildFinalResponse:
+    """
+    _build_final_response: final_output None; stream vs non-stream.
+    """
+
+    @pytest.mark.smoke
+    def test_build_final_response_none_returns_none(self, backend):
+        """
+        final_output is None -> return None.
+        ## WRITTEN BY AI ##
+        """
+        from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler
+
+        request = GenerationRequest(columns={"text_column": ["x"]})
+        request_info = RequestInfo()
+        ctx = _RequestContext(mode="chat", body={}, stream=True, files={})
+        handler = VLLMResponseHandler()
+        result = backend._build_final_response(
+            request, request_info, None, ctx, handler, stream=True
+        )
+        assert result is None
+
+    @pytest.mark.smoke
+    def test_build_final_response_stream_returns_compile_streaming(self, backend):
+        """
+        Stream path uses compile_streaming.
+        ## WRITTEN BY AI ##
+        """
+        from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler
+
+        mock_final = Mock()
+        mock_final.prompt_token_ids = [1, 2]
+        mock_final.outputs = [Mock(token_ids=[3, 4], text="ab")]
+        mock_final.request_id = "req-1"
+        request = GenerationRequest(columns={"text_column": ["x"]})
+        request_info = RequestInfo()
+        ctx = _RequestContext(mode="chat", body={}, stream=True, files={})
+        handler = VLLMResponseHandler()
+        handler.add_streaming_line('data: {"choices": [{"delta": {"content": "ab"}}]}')
+        result = backend._build_final_response(
+            request,
+            request_info,
+            mock_final,
+            ctx,
+            handler,
+            stream=True,
+            accumulated_text="ab",
+            total_output_tokens=2,
+        )
+        assert result is not None
+        resp, info = result
+        assert isinstance(resp, GenerationResponse)
+        assert resp.text == "ab"
+        assert info is request_info
+
+    @pytest.mark.smoke
+    def test_build_final_response_non_stream_returns_compile_non_streaming(
+        self, backend
+    ):
+        """
+        Non-stream: convert to OpenAI format and compile_non_streaming.
+        ## WRITTEN BY AI ##
+        """
+        from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler
+
+        mock_final = Mock()
+        mock_final.prompt_token_ids = [1, 2]
+        mock_final.outputs = [Mock(token_ids=[3, 4], text="hello")]
+        mock_final.request_id = "req-1"
+        request = GenerationRequest(columns={"text_column": ["x"]})
+        request_info = RequestInfo()
+        ctx = _RequestContext(mode="text", body={}, stream=False, files={})
+        handler = VLLMResponseHandler()
+        result = backend._build_final_response(
+            request, request_info, mock_final, ctx, handler, stream=False
+        )
+        assert result is not None
+        resp, info = result
+        assert isinstance(resp, GenerationResponse)
+        assert resp.text == "hello"
+        assert info is request_info
+
+
+class TestVLLMExtractTextFromContent:
+    """
+    _extract_text_from_content: string, list of text blocks, fallback.
+    """
+
+    @pytest.mark.smoke
+    def test_extract_text_from_content_string_returns_same(self, backend):
+        """
+        String -> same string.
+        ## WRITTEN BY AI ##
+        """
+        assert backend._extract_text_from_content("hello") == "hello"
+
+    @pytest.mark.smoke
+    def test_extract_text_from_content_list_text_blocks_concatenated(self, backend):
+        """
+        List of {type:text, text:x} -> concatenated.
+        ## WRITTEN BY AI ##
+        """
+        content = [
+            {"type": "text", "text": "a"},
+            {"type": "image_url", "url": "x"},
+            {"type": "text", "text": "b"},
+        ]
+        assert backend._extract_text_from_content(content) == "ab"
+
+    @pytest.mark.smoke
+    def test_extract_text_from_content_list_no_text_blocks_returns_empty(self, backend):
+        """
+        List without text blocks -> "".
+        ## WRITTEN BY AI ##
+        """
+        content = [{"type": "image_url", "url": "x"}]
+        assert backend._extract_text_from_content(content) == ""
+
+    @pytest.mark.smoke
+    def test_extract_text_from_content_fallback_str(self, backend):
+        """
+        Non-str non-list -> str(content).
+        ## WRITTEN BY AI ##
+        """
+        assert backend._extract_text_from_content(123) == "123"
+
+    @pytest.mark.smoke
+    def test_extract_text_from_content_none_returns_empty(self, backend):
+        """
+        content None -> "".
+        ## WRITTEN BY AI ##
+        """
+        assert backend._extract_text_from_content(None) == ""
+
+
+class TestVLLMExtractAudioFromRequest:
+    """
+    _extract_audio_from_request: empty, no file, tuple, raw bytes, non-bytes, invalid.
+    """
+
+    @pytest.mark.smoke
+    def test_extract_audio_empty_files_raises(self, backend):
+        """
+        Empty files -> ValueError.
+        ## WRITTEN BY AI ##
+        """
+        with pytest.raises(ValueError, match="Audio request must include audio file"):
+            backend._extract_audio_from_request({})
+
+    @pytest.mark.smoke
+    def test_extract_audio_no_valid_file_raises(self, backend):
+        """
+        No tuple/bytes in values -> ValueError.
+        ## WRITTEN BY AI ##
+        """
+        with pytest.raises(ValueError, match="Audio request must include audio file"):
+            backend._extract_audio_from_request({"k": "not-bytes"})
+
+    @pytest.mark.smoke
+    def test_extract_audio_tuple_returns_bytes_and_mimetype(self, backend):
+        """
+        Tuple (name, bytes, mimetype) -> return bytes and mimetype.
+        ## WRITTEN BY AI ##
+        """
+        data = b"audio-bytes"
+        out = backend._extract_audio_from_request({"f": ("x.wav", data, "audio/wav")})
+        assert out == (data, "audio/wav")
+
+    @pytest.mark.smoke
+    def test_extract_audio_tuple_two_elements_default_mimetype(self, backend):
+        """
+        Tuple (name, bytes) -> mimetype default audio/wav.
+        ## WRITTEN BY AI ##
+        """
+        data = b"audio"
+        out = backend._extract_audio_from_request({"f": ("x.wav", data)})
+        assert out == (data, "audio/wav")
+
+    @pytest.mark.smoke
+    def test_extract_audio_raw_bytes_returns_wav_tuple(self, backend):
+        """
+        Raw bytes value -> ("audio.wav", bytes, "audio/wav").
+        ## WRITTEN BY AI ##
+        """
+        data = b"raw"
+        out = backend._extract_audio_from_request({"f": data})
+        assert out == (data, "audio/wav")
+
+    @pytest.mark.smoke
+    def test_extract_audio_tuple_non_bytes_at_index_1_raises(self, backend):
+        """
+        Tuple with non-bytes at index 1 -> ValueError.
+        ## WRITTEN BY AI ##
+        """
+        with pytest.raises(ValueError, match="Expected bytes for audio data"):
+            backend._extract_audio_from_request(
+                {"f": ("x.wav", "not-bytes", "audio/wav")}
+            )
+
+
+class TestVLLMExtractPromptEdgeCases:
+    """
+    _extract_prompt: text/chat empty -> ValueError; audio optional prompt.
+    """
+
+    @pytest.mark.smoke
+    def test_extract_prompt_text_empty_prompt_raises(self, backend):
+        """
+        Text mode: body prompt missing or empty -> ValueError.
+        ## WRITTEN BY AI ##
+        """
+        with pytest.raises(ValueError, match="Text request must include 'prompt'"):
+            backend._extract_prompt({"prompt": ""}, "text")
+        with pytest.raises(ValueError, match="Text request must include 'prompt'"):
+            backend._extract_prompt({}, "text")
+
+    @pytest.mark.smoke
+    def test_extract_prompt_chat_empty_messages_raises(self, backend):
+        """
+        Chat mode: messages empty -> ValueError.
+        ## WRITTEN BY AI ##
+        """
+        with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"):
+            backend_plain = VLLMPythonBackend(
+                model="test-model", request_format="plain"
+            )
+            backend_plain._engine = None
+        with pytest.raises(ValueError, match="Chat request must include 'messages'"):
+            backend_plain._extract_prompt({"messages": []}, "chat")
+
+    @pytest.mark.smoke
+    def test_extract_prompt_audio_returns_prompt_or_empty(self, backend):
+        """
+        Audio mode: return body.get("prompt", "") (optional prompt).
+        ## WRITTEN BY AI ##
+        """
+        assert backend._extract_prompt({"prompt": "caption"}, "audio") == "caption"
+        assert backend._extract_prompt({}, "audio") == ""
+        assert backend._extract_prompt({"prompt": ""}, "audio") == ""
+
+
+class TestVLLMConvertVllmOutputToOpenAIFormat:
+    """
+    _convert_vllm_output_to_openai_format: OpenAI-shaped dict with usage and id.
+    """
+
+    @pytest.mark.smoke
+    def test_convert_vllm_output_to_openai_format_includes_usage_and_id(self, backend):
+        """
+        Builds dict with mode-specific payload, usage, and id from output.request_id.
+        ## WRITTEN BY AI ##
+        """
+        mock_out = Mock()
+        mock_out.prompt_token_ids = [1, 2]
+        mock_out.outputs = [Mock(token_ids=[3], text="hi")]
+        mock_out.request_id = "vllm-req-123"
+        result = backend._convert_vllm_output_to_openai_format(mock_out, "chat")
+        assert result["choices"] == [
+            {"message": {"content": "hi", "role": "assistant"}}
+        ]
+        assert result["usage"] == {
+            "prompt_tokens": 2,
+            "completion_tokens": 1,
+            "total_tokens": 3,
+        }
+        assert result["id"] == "vllm-req-123"
+
+    @pytest.mark.smoke
+    def test_convert_vllm_output_to_openai_format_no_request_id_omits_id(self, backend):
+        """
+        When output has no request_id or falsy, id is omitted.
+        ## WRITTEN BY AI ##
+        """
+        mock_out = Mock()
+        mock_out.prompt_token_ids = []
+        mock_out.outputs = [Mock(token_ids=[], text="")]
+        mock_out.request_id = None
+        result = backend._convert_vllm_output_to_openai_format(mock_out, "text")
+        assert "choices" in result
+        assert "usage" in result
+        assert "id" not in result
+
+
+class TestVLLMResolveNonStream:
+    """
+    resolve() with stream=False: one generate yield, compile_non_streaming.
+    """
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_resolve_non_stream_yields_one_response(self, backend):
+        """
+        stream=False; mock generate yields once; one response via compile_non_streaming.
+        ## WRITTEN BY AI ##
+        """
+        out = Mock()
+        out.text = "done"
+        out.token_ids = [1, 2, 3]
+        out.finish_reason = "stop"
+        req_out = Mock()
+        req_out.prompt_token_ids = [1, 2]
+        req_out.outputs = [out]
+        req_out.request_id = "r1"
+
+        async def one_yield(prompt, sampling_params, request_id):
+            yield req_out
+
+        backend._engine = Mock()
+        backend._engine.generate = one_yield
+        request = Mock(spec=GenerationRequest)
+        request.columns = None
+        request.arguments = Mock()
+        request.arguments.body = {"messages": [{"role": "user", "content": "Hi"}]}
+        request.arguments.stream = False
+        request.arguments.files = {}
+        request.output_metrics = UsageMetrics()
+        request.request_id = "req-1"
+        request.arguments.model_dump_json = Mock(return_value="{}")
+        request_info = RequestInfo()
+
+        results = []
+        with patch.object(
+            backend, "_extract_prompt", return_value="User: Hi\nAssistant: "
+        ):
+            async for response, info in backend.resolve(request, request_info):
+                results.append((response, info))
+
+        assert len(results) == 1
+        resp, info = results[0]
+        assert isinstance(resp, GenerationResponse)
+        assert resp.text == "done"
+        assert info is request_info
+
+
+class TestVLLMResolveValidation:
+    """
+    resolve(): history -> NotImplementedError; _engine None -> RuntimeError.
+    """
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_resolve_with_history_raises_not_implemented(self, backend):
+        """
+        history=[(...)] -> NotImplementedError.
+        ## WRITTEN BY AI ##
+        """
+        backend._engine = Mock()
+        request = GenerationRequest(columns={"text_column": ["Hi"]})
+        request_info = RequestInfo()
+        history = [(Mock(), Mock())]
+        with pytest.raises(
+            NotImplementedError, match="Multi-turn requests not yet supported"
+        ):
+            async for _ in backend.resolve(request, request_info, history=history):
+                pass
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_resolve_engine_none_raises_runtime_error(self, backend):
+        """
+        _engine is None -> RuntimeError.
+        ## WRITTEN BY AI ##
+        """
+        backend._engine = None
+        request = GenerationRequest(columns={"text_column": ["Hi"]})
+        request_info = RequestInfo()
+        with pytest.raises(RuntimeError, match="Backend not started up"):
+            async for _ in backend.resolve(request, request_info):
+                pass
+
+
+class TestVLLMResolveCancelledError:
+    """
+    resolve(): CancelledError after yield; response still yielded, then re-raise.
+    """
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_resolve_cancelled_error_yields_then_reraises(self, backend):
+        """
+        During generate raise CancelledError; assert response yielded then re-raise.
+        ## WRITTEN BY AI ##
+        """
+        out = Mock()
+        out.text = "partial"
+        out.token_ids = [1]
+        out.finish_reason = None
+        req_out = Mock()
+        req_out.prompt_token_ids = [1]
+        req_out.outputs = [out]
+        req_out.request_id = "r1"
+
+        async def yield_then_cancel(prompt, sampling_params, request_id):
+            yield req_out
+            raise asyncio.CancelledError
+
+        backend._engine = Mock()
+        backend._engine.generate = yield_then_cancel
+        request = GenerationRequest(columns={"text_column": ["Hi"]})
+        request_info = RequestInfo()
+        results = []
+        async def collect():
+            async for response, info in backend.resolve(request, request_info):
+                results.append((response, info))
+
+        with patch.object(
+            backend, "_extract_prompt", return_value="User: Hi\nAssistant: "
+        ), pytest.raises(asyncio.CancelledError):
+            await collect()
+        assert len(results) == 1
+        assert results[0][0].text == "partial"
+
+
+class TestVLLMResolveAudioError:
+    """
+    resolve(): At most 0 audio / audio(s) may be provided -> RuntimeError with guidance.
+    """
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_resolve_audio_error_wraps_with_guidance(self, backend):
+        """
+        'At most 0 audio' -> RuntimeError with audio-capable model guidance.
+        ## WRITTEN BY AI ##
+        """
+
+        async def mock_generate_raise(*args, **kwargs):
+            raise ValueError("At most 0 audio(s) may be provided.")
+            yield  # makes this an async generator so async for raises
+
+        with patch("guidellm.backends.vllm_python.vllm._decode_audio") as mock_decode:
+            mock_decode.return_value = Mock(data=np.array([0.0]))
+            backend._engine = Mock()
+            backend._engine.generate = mock_generate_raise
+            request = Mock(spec=GenerationRequest)
+            request.columns = None
+            request.arguments = Mock()
+            request.arguments.body = {}
+            request.arguments.stream = False
+            request.arguments.files = {"f": ("audio.wav", b"audio", "audio/wav")}
+            request.output_metrics = UsageMetrics()
+            request.request_id = "r1"
+            request_info = RequestInfo()
+            with pytest.raises(RuntimeError) as exc_info:
+                async for _ in backend.resolve(request, request_info):
+                    pass
+            assert "does not support audio" in str(exc_info.value)
+            assert "audio-capable model" in str(exc_info.value)
+
+
+class TestVLLMResolveAudioMode:
+    """
+    resolve() audio: mock _decode_audio and generate; one response.
+    """
+
+    @pytest.mark.asyncio
+    @pytest.mark.smoke
+    async def test_resolve_audio_mode_yields_one_response(self, backend):
+        """
+        Mode audio; mock _decode_audio and engine.generate; assert one response.
+        ## WRITTEN BY AI ##
+        """
+        mock_audio_array = np.array([0.0, 0.1])
+        mock_decode_result = Mock()
+        mock_decode_result.data = mock_audio_array
+
+        out = Mock()
+        out.text = "transcribed"
+        out.token_ids = [1, 2]
+        out.finish_reason = "stop"
+        req_out = Mock()
+        req_out.prompt_token_ids = [1]
+        req_out.outputs = [out]
+        req_out.request_id = "r1"
+
+        async def mock_generate(prompt, sampling_params, request_id):
+            assert isinstance(prompt, dict)
+            assert "prompt" in prompt
+            assert "multi_modal_data" in prompt
+            assert "audio" in prompt["multi_modal_data"]
+            yield req_out
+
+        with patch(
+            "guidellm.backends.vllm_python.vllm._decode_audio",
+            return_value=mock_decode_result,
+        ):
+            backend._engine = Mock()
+            backend._engine.generate = mock_generate
+            request = Mock(spec=GenerationRequest)
+            request.columns = None
+            request.arguments = Mock()
+            request.arguments.body = {"prompt": ""}
+            request.arguments.stream = False
+            request.arguments.files = {"f": ("audio.wav", b"audio-bytes", "audio/wav")}
+            request.output_metrics = UsageMetrics()
+            request.request_id = "r1"
+            request.arguments.model_dump_json = Mock(return_value="{}")
+            request_info = RequestInfo()
+            results = []
+            async for response, info in backend.resolve(request, request_info):
+                results.append((response, info))
+            assert len(results) == 1
+            assert results[0][0].text == "transcribed"
diff --git a/tests/unit/backends/vllm_python/test_vllm_response.py b/tests/unit/backends/vllm_python/test_vllm_response.py
new file mode 100644
index 000000000..8240c6669
--- /dev/null
+++ b/tests/unit/backends/vllm_python/test_vllm_response.py
@@ -0,0 +1,225 @@
+"""
+Unit tests for VLLMResponseHandler.
+
+Tests compilation of OpenAI-style response dicts (choices+usage and text+usage)
+and streaming SSE line parsing without request type.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler
+from guidellm.schemas import GenerationRequest, GenerationResponse
+
+
+@pytest.fixture
+def request_without_arguments():
+    """GenerationRequest with no arguments (columns only)."""
+    return GenerationRequest(columns={"text_column": ["test"]})
+
+
+@pytest.fixture
+def handler():
+    """Fresh VLLMResponseHandler instance."""
+    return VLLMResponseHandler()
+
+
+class TestVLLMResponseHandler:
+    """
+    Test cases for VLLMResponseHandler.
+    ## WRITTEN BY AI ##
+    """
+
+    @pytest.mark.smoke
+    def test_has_required_methods(self):
+        """
+        Handler exposes compile_non_streaming, add_streaming_line, compile_streaming.
+        ## WRITTEN BY AI ##
+        """
+        assert hasattr(VLLMResponseHandler, "compile_non_streaming")
+        assert hasattr(VLLMResponseHandler, "add_streaming_line")
+        assert hasattr(VLLMResponseHandler, "compile_streaming")
+
+    @pytest.mark.smoke
+    def test_compile_non_streaming_choices_usage(
+        self, handler, request_without_arguments
+    ):
+        """
+        compile_non_streaming with choices+usage (text/chat) shape.
+        ## WRITTEN BY AI ##
+        """
+        response_dict = {
+            "id": "gen-123",
+            "choices": [{"text": "Hello world"}],
+            "usage": {
+                "prompt_tokens": 5,
+                "completion_tokens": 2,
+                "total_tokens": 7,
+            },
+        }
+        out = handler.compile_non_streaming(request_without_arguments, response_dict)
+        assert isinstance(out, GenerationResponse)
+        assert out.request_id == request_without_arguments.request_id
+        assert out.response_id == "gen-123"
+        assert out.text == "Hello world"
+        assert out.input_metrics.text_tokens == 5
+        assert out.output_metrics.text_tokens == 2
+        assert out.output_metrics.text_words == 2
+        assert out.output_metrics.text_characters == 11
+
+    @pytest.mark.smoke
+    def test_compile_non_streaming_choices_message(
+        self, handler, request_without_arguments
+    ):
+        """
+        compile_non_streaming with choices[].message (chat) shape.
+        ## WRITTEN BY AI ##
+        """
+        response_dict = {
+            "choices": [{"message": {"content": "Hi there", "role": "assistant"}}],
+            "usage": {"prompt_tokens": 1, "completion_tokens": 2},
+        }
+        out = handler.compile_non_streaming(request_without_arguments, response_dict)
+        assert out.text == "Hi there"
+        assert out.input_metrics.text_tokens == 1
+        assert out.output_metrics.text_tokens == 2
+
+    @pytest.mark.smoke
+    def test_compile_non_streaming_text_usage(self, handler, request_without_arguments):
+        """
+        compile_non_streaming with text+usage (audio) shape.
+        ## WRITTEN BY AI ##
+        """
+        response_dict = {
+            "text": "Transcribed text",
+            "usage": {"prompt_tokens": 0, "completion_tokens": 3},
+        }
+        out = handler.compile_non_streaming(request_without_arguments, response_dict)
+        assert out.text == "Transcribed text"
+        assert out.output_metrics.text_tokens == 3
+
+    @pytest.mark.smoke
+    def test_compile_non_streaming_empty_usage(
+        self, handler, request_without_arguments
+    ):
+        """
+        compile_non_streaming with no usage still fills text and word/char counts.
+        ## WRITTEN BY AI ##
+        """
+        response_dict = {"choices": [{"text": "Hi"}]}
+        out = handler.compile_non_streaming(request_without_arguments, response_dict)
+        assert out.text == "Hi"
+        assert out.output_metrics.text_words == 1
+        assert out.output_metrics.text_characters == 2
+
+    @pytest.mark.smoke
+    def test_add_streaming_line_choices_text(self, handler):
+        """
+        add_streaming_line parses choices[].text delta.
+        ## WRITTEN BY AI ##
+        """
+        line = 'data: {"choices": [{"text": "ab"}]}'
+        assert handler.add_streaming_line(line) == 1
+        assert handler.add_streaming_line('data: {"choices": [{"text": "c"}]}') == 1
+        assert handler.streaming_texts == ["ab", "c"]
+
+    @pytest.mark.smoke
+    def test_add_streaming_line_choices_delta(self, handler):
+        """
+        add_streaming_line parses choices[].delta.content (chat) delta.
+        ## WRITTEN BY AI ##
+        """
+        handler.add_streaming_line('data: {"choices": [{"delta": {"content": "x"}}]}')
+        handler.add_streaming_line('data: {"choices": [{"delta": {"content": "y"}}]}')
+        assert handler.streaming_texts == ["x", "y"]
+
+    @pytest.mark.smoke
+    def test_add_streaming_line_text(self, handler):
+        """
+        add_streaming_line parses audio-style text delta.
+        ## WRITTEN BY AI ##
+        """
+        handler.add_streaming_line('data: {"text": "hello"}')
+        assert handler.add_streaming_line('data: {"text": " world"}') == 1
+        assert handler.streaming_texts == ["hello", " world"]
+
+    @pytest.mark.smoke
+    def test_add_streaming_line_done_returns_none(self, handler):
+        """
+        add_streaming_line returns None for data: [DONE].
+        ## WRITTEN BY AI ##
+        """
+        assert handler.add_streaming_line("data: [DONE]") is None
+
+    @pytest.mark.smoke
+    def test_add_streaming_line_empty_returns_zero(self, handler):
+        """
+        add_streaming_line returns 0 for line with no content.
+        ## WRITTEN BY AI ##
+        """
+        assert handler.add_streaming_line("data: {}") == 0
+
+    @pytest.mark.smoke
+    def test_add_streaming_line_invalid_json_returns_zero(self, handler):
+        """
+        add_streaming_line with invalid JSON in data returns 0; no crash.
+        ## WRITTEN BY AI ##
+        """
+        result = handler.add_streaming_line("data: { invalid }")
+        assert result == 0
+        assert handler.streaming_texts == []
+
+    @pytest.mark.smoke
+    def test_compile_streaming_uses_accumulated_text(
+        self, handler, request_without_arguments
+    ):
+        """
+        compile_streaming builds response from accumulated chunks.
+        ## WRITTEN BY AI ##
+        """
+        handler.add_streaming_line('data: {"choices": [{"text": "Hello"}]}')
+        handler.add_streaming_line('data: {"choices": [{"text": " "}]}')
+        handler.add_streaming_line('data: {"choices": [{"text": "world"}]}')
+        out = handler.compile_streaming(request_without_arguments)
+        assert out.text == "Hello world"
+        assert out.request_id == request_without_arguments.request_id
+
+    @pytest.mark.smoke
+    def test_compile_streaming_stores_response_id(
+        self, handler, request_without_arguments
+    ):
+        """
+        compile_streaming uses response_id from streaming chunks.
+        ## WRITTEN BY AI ##
+        """
+        handler.add_streaming_line(
+            'data: {"id": "stream-1", "choices": [{"text": "x"}]}'
+        )
+        out = handler.compile_streaming(request_without_arguments)
+        assert out.response_id == "stream-1"
+        assert out.text == "x"
+
+    @pytest.mark.smoke
+    def test_request_args_from_arguments_if_present(self, request_without_arguments):
+        """
+        When request has arguments, request_args is serialized in response.
+        ## WRITTEN BY AI ##
+        """
+        from unittest.mock import Mock
+
+        # Request has no 'arguments'; handler uses getattr(request, "arguments", None).
+        # Mock request with arguments.model_dump_json (e.g. from alternate pipelines).
+        request_with_arguments = Mock(spec=GenerationRequest)
+        request_with_arguments.request_id = request_without_arguments.request_id
+        request_with_arguments.arguments = Mock()
+        request_with_arguments.arguments.model_dump_json.return_value = (
+            '{"body":{"prompt":"hi","max_tokens":10}}'
+        )
+
+        response_dict = {"choices": [{"text": "hey"}], "usage": {}}
+        handler = VLLMResponseHandler()
+        out = handler.compile_non_streaming(request_with_arguments, response_dict)
+        assert out.request_args is not None
+        assert "prompt" in out.request_args
+        assert "max_tokens" in out.request_args

From 1c8cba8d0afeb10b0d6c84b23586cad4b5924b1b Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 16 Feb 2026 17:32:57 -0500
Subject: [PATCH 48/56] Added containerfile for vLLM Python

Generated-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile.vllm | 49 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)
 create mode 100644 Containerfile.vllm

diff --git a/Containerfile.vllm b/Containerfile.vllm
new file mode 100644
index 000000000..bee178bd4
--- /dev/null
+++ b/Containerfile.vllm
@@ -0,0 +1,49 @@
+# Base image: vllm/vllm-openai (has vLLM pre-installed). Override with build arg.
+ARG BASE_IMAGE=vllm/vllm-openai
+
+FROM $BASE_IMAGE
+
+# release: take the last version and add a post if build iteration
+# candidate: increment to next minor, add 'rc' with build iteration
+# nightly: increment to next minor, add 'a' with build iteration
+# alpha: increment to next minor, add 'a' with build iteration
+# dev: increment to next minor, add 'dev' with build iteration
+ARG GUIDELLM_BUILD_TYPE=dev
+
+# Extra dependencies to install (e.g. recommended, all)
+ARG GUIDELLM_BUILD_EXTRAS=recommended
+
+# Switch to root for installing system deps and pip install
+USER root
+
+# Install git for setuptools-git-versioning (version discovery)
+RUN apt-get update && apt-get install -y --no-install-recommends git \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV GUIDELLM_BUILD_TYPE=$GUIDELLM_BUILD_TYPE
+
+# Copy repository and install GuideLLM from source with pip (no uv, to avoid
+# conflicting with the pre-installed vLLM in the base image)
+COPY / /src
+WORKDIR /src
+RUN pip install --no-cache-dir ".[${GUIDELLM_BUILD_EXTRAS}]"
+
+# Metadata
+LABEL io.k8s.display-name="GuideLLM" \
+      org.opencontainers.image.description="GuideLLM Performance Benchmarking Container" \
+      org.opencontainers.image.source="https://github.com/vllm-project/guidellm" \
+      org.opencontainers.image.documentation="https://blog.vllm.ai/guidellm/stable" \
+      org.opencontainers.image.license="Apache-2.0"
+
+ENV HOME="/home/guidellm" \
+    GUIDELLM_OUTPUT_DIR="/results"
+
+WORKDIR $HOME
+
+VOLUME /results
+
+# Root group for k8s
+USER 1001:0
+
+ENTRYPOINT [ "guidellm" ]
+CMD [ "benchmark", "run" ]

From 1d69eaccea99ab9eba956e604289257578f7ae8e Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 16 Feb 2026 18:41:21 -0500
Subject: [PATCH 49/56] Fix vllm container and make audio dependency optional
 for vllm-python backend

Assisted-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile.vllm                           | 2 +-
 src/guidellm/backends/vllm_python/vllm.py    | 8 +++++++-
 tests/unit/backends/vllm_python/test_vllm.py | 4 ++--
 3 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/Containerfile.vllm b/Containerfile.vllm
index bee178bd4..213883a6b 100644
--- a/Containerfile.vllm
+++ b/Containerfile.vllm
@@ -11,7 +11,7 @@ FROM $BASE_IMAGE
 ARG GUIDELLM_BUILD_TYPE=dev
 
 # Extra dependencies to install (e.g. recommended, all)
-ARG GUIDELLM_BUILD_EXTRAS=recommended
+ARG GUIDELLM_BUILD_EXTRAS=recommended,audio
 
 # Switch to root for installing system deps and pip install
 USER root
diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 36726b28c..f14927351 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -20,7 +20,6 @@
 
 from guidellm.backends.backend import Backend
 from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler
-from guidellm.extras.audio import _decode_audio
 from guidellm.logger import logger
 from guidellm.schemas import (
     GenerationRequest,
@@ -811,6 +810,13 @@ def _build_audio_generate_input(
             )
         if len(audio_bytes) == 0:
             raise ValueError("Audio data cannot be empty")
+        try:
+            from guidellm.extras.audio import _decode_audio
+        except ImportError as e:
+            raise ImportError(
+                "Audio support requires guidellm[audio] (torchcodec). "
+                "Install with: pip install 'guidellm[audio]'"
+            ) from e
         try:
             audio_samples = _decode_audio(audio_bytes)
             if hasattr(audio_samples.data, "numpy"):
diff --git a/tests/unit/backends/vllm_python/test_vllm.py b/tests/unit/backends/vllm_python/test_vllm.py
index 9a75223f6..194c3a959 100644
--- a/tests/unit/backends/vllm_python/test_vllm.py
+++ b/tests/unit/backends/vllm_python/test_vllm.py
@@ -1394,7 +1394,7 @@ async def mock_generate_raise(*args, **kwargs):
             raise ValueError("At most 0 audio(s) may be provided.")
             yield  # makes this an async generator so async for raises
 
-        with patch("guidellm.backends.vllm_python.vllm._decode_audio") as mock_decode:
+        with patch("guidellm.extras.audio._decode_audio") as mock_decode:
             mock_decode.return_value = Mock(data=np.array([0.0]))
             backend._engine = Mock()
             backend._engine.generate = mock_generate_raise
@@ -1447,7 +1447,7 @@ async def mock_generate(prompt, sampling_params, request_id):
             yield req_out
 
         with patch(
-            "guidellm.backends.vllm_python.vllm._decode_audio",
+            "guidellm.extras.audio._decode_audio",
             return_value=mock_decode_result,
         ):
             backend._engine = Mock()

From c94235fa5da1f4112630cc011c54b2da91afee17 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Tue, 17 Feb 2026 14:41:47 -0500
Subject: [PATCH 50/56] Fix permission issues in home dir

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 Containerfile.vllm | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/Containerfile.vllm b/Containerfile.vllm
index 213883a6b..f86def8ca 100644
--- a/Containerfile.vllm
+++ b/Containerfile.vllm
@@ -40,6 +40,10 @@ ENV HOME="/home/guidellm" \
 
 WORKDIR $HOME
 
+# Ensure that the user home dir can be used by any user
+# (OpenShift Pods can't use the cache otherwise)
+RUN chgrp -R 0 "$HOME" && chmod -R g=u "$HOME"
+
 VOLUME /results
 
 # Root group for k8s

From 7cbff80dd3e6993c8eafbb5c710130d2a51b9dc7 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Tue, 17 Feb 2026 14:54:33 -0500
Subject: [PATCH 51/56] Handle CPU-only case more gracefully

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/vllm_python/vllm.py    | 28 ++++++---
 tests/unit/backends/vllm_python/test_vllm.py | 66 ++++++++++++++++++++
 2 files changed, 85 insertions(+), 9 deletions(-)

diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index f14927351..7d842c845 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -17,6 +17,7 @@
 from typing import Any, Literal, cast
 
 import numpy as np
+import torch
 
 from guidellm.backends.backend import Backend
 from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler
@@ -84,10 +85,13 @@ class VLLMPythonBackend(Backend):
     """
     Python API backend for VLLM inference engine.
 
-    Directly uses VLLM's AsyncLLMEngine for local async inference. VLLM automatically
-    uses CUDA if available, otherwise falls back to CPU. Handles request/response
-    conversion between GuideLLM schemas and VLLM's native API, with async support for
-    finer token-by-token processing and timings.
+    Directly uses VLLM's AsyncLLMEngine for local async inference. When CUDA is not
+    available and ``device`` is not set in vllm_config, the backend sets
+    ``device="cpu"`` so the engine runs on CPU; otherwise vLLM uses CUDA if
+    available. You can pass ``device`` in vllm_config (e.g. ``"cpu"``, ``"cuda"``)
+    and it is passed through to AsyncEngineArgs. Handles request/response conversion
+    between GuideLLM schemas and VLLM's native API, with async support for finer
+    token-by-token processing and timings.
 
     Engine parameters not set in vllm_config use vLLM's AsyncEngineArgs defaults.
     Example (optional overrides):
@@ -137,11 +141,12 @@ def __init__(
         :param model: Model identifier or path for VLLM to load
         :param vllm_config: Optional dict of VLLM AsyncEngineArgs parameters.
             Passed through with no GuideLLM defaults; only model (and optionally
-            chat_template) are set by the backend. Unset parameters use vLLM's
-            defaults. Common options include tensor_parallel_size,
-            gpu_memory_utilization, max_model_len, and any other parameter
-            accepted by vllm.AsyncEngineArgs. VLLM uses CUDA if available, else CPU.
-            The 'device' parameter is not supported and will be ignored.
+            chat_template) are set by the backend. When CUDA is not available and
+            ``device`` is not set here, the backend sets ``device="cpu"``. You can
+            pass ``device`` (e.g. ``"cpu"``, ``"cuda"``) and it is passed through.
+            Unset parameters use vLLM's defaults. Common options include
+            tensor_parallel_size, gpu_memory_utilization, max_model_len, and any
+            other parameter accepted by vllm.AsyncEngineArgs.
         :param request_format: "plain" (no chat template), "default-template"
             (use tokenizer default), or a chat template path / single-line string.
         :param target: Target URL (ignored for VLLM Python backend, runs locally)
@@ -194,6 +199,11 @@ def _merge_config(self, user_config: dict[str, Any]) -> dict[str, Any]:
         ):
             config["chat_template"] = self.request_format
 
+        # When CUDA is not available, set device to CPU so vLLM does not rely on
+        # auto-detection (which can yield an empty device_type and raise).
+        if "device" not in config and not torch.cuda.is_available():
+            config["device"] = "cpu"
+
         return config
 
     @property
diff --git a/tests/unit/backends/vllm_python/test_vllm.py b/tests/unit/backends/vllm_python/test_vllm.py
index 194c3a959..aa664b288 100644
--- a/tests/unit/backends/vllm_python/test_vllm.py
+++ b/tests/unit/backends/vllm_python/test_vllm.py
@@ -369,6 +369,72 @@ def test_vllm_config_empty_uses_vllm_defaults(self):
             assert "gpu_memory_utilization" not in b.vllm_config
 
 
+class TestVLLMMergeConfigDevice:
+    """
+    Test _merge_config device logic: default to CPU when CUDA is unavailable.
+    ## WRITTEN BY AI ##
+    """
+
+    @pytest.mark.smoke
+    def test_cuda_unavailable_no_device_sets_cpu(self):
+        """
+        When torch.cuda.is_available() is False and user does not set device,
+        merged config includes device="cpu".
+        ## WRITTEN BY AI ##
+        """
+        with (
+            patch("guidellm.backends.vllm_python.vllm._check_vllm_available"),
+            patch(
+                "guidellm.backends.vllm_python.vllm.torch.cuda.is_available",
+                return_value=False,
+            ),
+        ):
+            backend = VLLMPythonBackend(model="test-model")
+        assert backend.vllm_config.get("device") == "cpu"
+        assert backend.vllm_config.get("model") == "test-model"
+
+    @pytest.mark.smoke
+    def test_cuda_unavailable_user_sets_device_preserved(self):
+        """
+        When torch.cuda.is_available() is False and user sets device,
+        merged config keeps the user's device (no override).
+        ## WRITTEN BY AI ##
+        """
+        with (
+            patch("guidellm.backends.vllm_python.vllm._check_vllm_available"),
+            patch(
+                "guidellm.backends.vllm_python.vllm.torch.cuda.is_available",
+                return_value=False,
+            ),
+        ):
+            backend_cpu = VLLMPythonBackend(
+                model="test-model", vllm_config={"device": "cpu"}
+            )
+            backend_cuda = VLLMPythonBackend(
+                model="test-model", vllm_config={"device": "cuda"}
+            )
+        assert backend_cpu.vllm_config.get("device") == "cpu"
+        assert backend_cuda.vllm_config.get("device") == "cuda"
+
+    @pytest.mark.smoke
+    def test_cuda_available_no_device_not_added(self):
+        """
+        When torch.cuda.is_available() is True and user does not set device,
+        merged config does not add device (vLLM auto-detection is used).
+        ## WRITTEN BY AI ##
+        """
+        with (
+            patch("guidellm.backends.vllm_python.vllm._check_vllm_available"),
+            patch(
+                "guidellm.backends.vllm_python.vllm.torch.cuda.is_available",
+                return_value=True,
+            ),
+        ):
+            backend = VLLMPythonBackend(model="test-model")
+        assert "device" not in backend.vllm_config
+        assert backend.vllm_config.get("model") == "test-model"
+
+
 class TestVLLMExtractPromptTokenizerWarnings:
     """
     Test warnings when tokenizer is set but not used for prompt extraction.

From 7cef453541f45e5faabf8c8c270a1afe16603232 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 18 Feb 2026 00:01:28 -0500
Subject: [PATCH 52/56] Added documentation for vllm python backend

Assisted-by: Cursor AI
Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 docs/getting-started/benchmark.md  |  2 +-
 docs/getting-started/install.md    |  2 +
 docs/guides/backends.md            |  4 ++
 docs/guides/multimodal/audio.md    |  4 +-
 docs/guides/multimodal/image.md    |  4 +-
 docs/guides/multimodal/video.md    |  4 +-
 docs/guides/vllm-python-backend.md | 68 ++++++++++++++++++++++++++++++
 7 files changed, 81 insertions(+), 7 deletions(-)
 create mode 100644 docs/guides/vllm-python-backend.md

diff --git a/docs/getting-started/benchmark.md b/docs/getting-started/benchmark.md
index 11a04d19a..2d46df3c6 100644
--- a/docs/getting-started/benchmark.md
+++ b/docs/getting-started/benchmark.md
@@ -4,7 +4,7 @@ weight: -6
 
 # Run a Benchmark
 
-After [installing GuideLLM](install.md) and [starting a server](server.md), you're ready to run benchmarks to evaluate your LLM deployment's performance.
+After [installing GuideLLM](install.md) and [starting a server](server.md), you're ready to run benchmarks to evaluate your LLM deployment's performance. Alternatively, you can run benchmarks with the vLLM Python backend (`--backend vllm_python`) without a separate server; see [vLLM Python backend](../guides/vllm-python-backend.md).
 
 Running a GuideLLM benchmark is straightforward. The basic command structure is:
 
diff --git a/docs/getting-started/install.md b/docs/getting-started/install.md
index 73421ecc7..4e47e2f64 100644
--- a/docs/getting-started/install.md
+++ b/docs/getting-started/install.md
@@ -86,6 +86,8 @@ guidellm --help
 
 This should display the installed version of GuideLLM.
 
+To use the vLLM Python backend (in-process inference), see [vLLM Python backend](../guides/vllm-python-backend.md) for recommended installation (container or existing vLLM environment) and pip installation notes.
+
 ## Troubleshooting
 
 If you encounter any issues during installation, ensure that your Python and pip versions meet the prerequisites. For further assistance, please refer to the [GitHub Issues](https://github.com/vllm-project/guidellm/issues) page or consult the [Documentation](https://github.com/vllm-project/guidellm/tree/main/docs).
diff --git a/docs/guides/backends.md b/docs/guides/backends.md
index 8696932bd..e4da624ba 100644
--- a/docs/guides/backends.md
+++ b/docs/guides/backends.md
@@ -8,6 +8,10 @@ GuideLLM is designed to work with OpenAI-compatible HTTP servers, enabling seaml
 
 GuideLLM supports OpenAI-compatible HTTP servers, which provide a standardized API for interacting with LLMs. This includes popular implementations such as [vLLM](https://github.com/vllm-project/vllm) and [Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference). These servers allow GuideLLM to perform evaluations, benchmarks, and optimizations with minimal setup.
 
+### vLLM Python backend
+
+GuideLLM supports running inference in the same process using the **vLLM Python backend** (`vllm_python`). This backend runs inference in the same process as GuideLLM's using vLLM's python API (AsyncLLMEngine), without an HTTP server. For setup, installation options (container, existing vLLM, pip), and examples, see [vLLM Python backend](vllm-python-backend.md).
+
 ## Examples for Spinning Up Compatible Servers
 
 ### 1. vLLM
diff --git a/docs/guides/multimodal/audio.md b/docs/guides/multimodal/audio.md
index 51c417de8..bbea507f9 100644
--- a/docs/guides/multimodal/audio.md
+++ b/docs/guides/multimodal/audio.md
@@ -106,10 +106,10 @@ For example, to specify French as the target language for an audio translation r
 
 #### "stream"
 
-Turn streaming responses on or off (if supported by the server) using a boolean value. By default, streaming is enabled.
+Turn streaming responses on or off (if supported by the backend) using a boolean value. By default, streaming is enabled. Use `--backend-kwargs`:
 
 ```bash
---request-formatter-kwargs '{"stream": false}'
+--backend-kwargs '{"stream": false}'
 ```
 
 ## Expected Results
diff --git a/docs/guides/multimodal/image.md b/docs/guides/multimodal/image.md
index b891eb1c2..912957ad8 100644
--- a/docs/guides/multimodal/image.md
+++ b/docs/guides/multimodal/image.md
@@ -97,10 +97,10 @@ For example, to specify a specific system prompt or other body parameter:
 
 #### "stream"
 
-Turn streaming responses on or off (if supported by the server) using a boolean value. By default, streaming is enabled.
+Turn streaming responses on or off (if supported by the backend) using a boolean value. By default, streaming is enabled. Use `--backend-kwargs`:
 
 ```bash
---request-formatter-kwargs '{"stream": false}'
+--backend-kwargs '{"stream": false}'
 ```
 
 ## Expected Results
diff --git a/docs/guides/multimodal/video.md b/docs/guides/multimodal/video.md
index 87e2a3cab..6f4085aa7 100644
--- a/docs/guides/multimodal/video.md
+++ b/docs/guides/multimodal/video.md
@@ -92,10 +92,10 @@ For example, to specify a specific system prompt or other body parameter:
 
 #### "stream"
 
-Turn streaming responses on or off (if supported by the server) using a boolean value. By default, streaming is enabled.
+Turn streaming responses on or off (if supported by the backend) using a boolean value. By default, streaming is enabled. Use `--backend-kwargs`:
 
 ```bash
---request-formatter-kwargs '{"stream": false}'
+--backend-kwargs '{"stream": false}'
 ```
 
 ## Expected Results
diff --git a/docs/guides/vllm-python-backend.md b/docs/guides/vllm-python-backend.md
new file mode 100644
index 000000000..b7b29074c
--- /dev/null
+++ b/docs/guides/vllm-python-backend.md
@@ -0,0 +1,68 @@
+# vLLM Python Backend
+
+The **vLLM Python backend** (`vllm_python`) runs inference in the **same process** as GuideLLM using vLLM's [AsyncLLMEngine](https://docs.vllm.ai/). No HTTP server is involved, reducing overheat and variables. This is useful for isolating performance bottlenecks or simplifying your benchmark setup. You do **not** pass `--target`; you **must** pass `--model`.
+
+For all engine options and supported models, see vLLM's [Engine Arguments](https://docs.vllm.ai/en/stable/configuration/engine_args/) and the [vLLM documentation](https://docs.vllm.ai/).
+
+## Installation
+
+### Recommended methods
+
+- **Official GuideLLM + vLLM image**  
+  Build and run the image that uses the vLLM base image (e.g. [Containerfile.vllm](https://github.com/vllm-project/guidellm/blob/main/Containerfile.vllm)). It is based on `vllm/vllm-openai` and installs GuideLLM on top, giving a known-good vLLM + GuideLLM stack with hardware support as provided by the base image.
+  
+  **Note:** This method will result in the preference for vllm's requirements as opposed to GuideLLM's requirements. Since vLLM is the more complex project, this is the recommended configuration, but this may result in an older Python or dependency version, resulting in sub-optimal GuideLLM performance and behavior in some scenarios.
+
+- **Existing vLLM installation**  
+  Install vLLM first for your environment (GPU/CPU, CUDA, etc.), then install GuideLLM in the same environment (e.g. `pip install guidellm` or with extras). You avoid a duplicate vLLM install and reuse your existing acceleration setup.
+  
+  **Note:** Using [uv](https://github.com/astral-sh/uv) is not recommended for the vLLM Python backend because of potentially incompatible requirements between the two projects. Prefer pip or the container / existing vLLM environment.
+
+
+It is also possible to install GuideLLM and vLLM via pip using `pip install guidellm[vllm]`. This method may make **hardware acceleration** (e.g. CUDA) harder to get working. See [vLLM installation](https://docs.vllm.ai/en/latest/getting_started/installation) and GPU/hardware-specific docs there. For production or GPU use, the container or existing-install path is recommended.
+
+
+## Basic example
+
+Run a benchmark with the vLLM Python backend (no `--target`):
+
+```bash
+guidellm benchmark run \
+  --backend vllm_python \
+  --model "ibm-granite/granite-4.0-h-350m" \
+  --data "prompt_tokens=256,output_tokens=128" \
+  --max-seconds 20 \
+  --rate 3
+```
+
+Engine behavior (device, memory, etc.) follows vLLM defaults unless you override it via `--backend-kwargs` (e.g. `vllm_config`). When running without a GPU (e.g. the GuideLLM + vLLM container without GPU access), the backend automatically uses the CPU device unless you set `device` in `vllm_config`. For engine configuration options, see vLLM's [Engine Arguments](https://docs.vllm.ai/en/stable/configuration/engine_args/).
+
+## Request format and backend options
+
+- **`--request-format`**  
+  Controls how chat prompts are built. Options: `plain` (no chat template; message content is concatenated as plain text), `default-template` (use the tokenizer’s default chat template), or a file path / single-line template string per vLLM’s supported options. The value is passed through to vLLM's chat template handling. For details, see vLLM's [Chat templates](https://docs.vllm.ai/en/latest/api/vllm/transformers_utils/chat_templates/) documentation.
+
+- **`--backend-kwargs`**  
+  Backend-specific options are passed here as a JSON object: pass a `vllm_config` key whose value is a dict of engine option names and values. You can also pass `request_format` here as an alternative to `--request-format`.
+
+  **Using Engine Arguments in `vllm_config`:** The [Engine Arguments](https://docs.vllm.ai/en/stable/configuration/engine_args/) documentation describes options in **CLI form** (e.g. `--gpu-memory-utilization`, `--max-model-len`). For `vllm_config` you must use the **Python parameter names** instead: strip the leading `--` and replace dashes with underscores (e.g. `gpu_memory_utilization`, `max_model_len`). The keys are the same as the field names on vLLM's `EngineArgs` and `AsyncEngineArgs` dataclasses; for the exact list of allowed keys and types, see the [vLLM source: `vllm/engine/arg_utils.py`](https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py) (search for `class EngineArgs`).
+
+  Example — limit GPU memory use and context length:
+
+  ```bash
+  --backend-kwargs '{"vllm_config": {"gpu_memory_utilization": 0.8, "max_model_len": 4096}}'
+  ```
+
+  For the full list of options and their types, see vLLM's [Engine Arguments](https://docs.vllm.ai/en/stable/configuration/engine_args/) (CLI form) and the [EngineArgs source](https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py) (Python field names for `vllm_config`).
+
+## See also
+
+- [Backends](backends.md) — Overview of supported backends.
+- [Run a benchmark](../getting-started/benchmark.md) — General benchmark options.
+- [vLLM Engine Arguments](https://docs.vllm.ai/en/stable/configuration/engine_args/) — CLI-oriented docs; use Python names (e.g. `gpu_memory_utilization`) in `vllm_config`.
+- [vLLM source: `vllm/engine/arg_utils.py`](https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py) — `EngineArgs` / `AsyncEngineArgs` field names and types for `vllm_config` keys.
+- [vLLM AsyncEngineArgs API](https://docs.vllm.ai/en/stable/api/vllm/engine/arg_utils/#vllm.engine.arg_utils.AsyncEngineArgs) — API reference for the class that receives these options.
+- [vLLM Chat templates](https://docs.vllm.ai/en/latest/api/vllm/transformers_utils/chat_templates/) — For `--request-format` behavior.
+- [vLLM documentation](https://docs.vllm.ai/)
+- [vLLM installation](https://docs.vllm.ai/en/latest/getting_started/installation)
+- [vLLM OpenAI-compatible server](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html) — When using the HTTP server instead of the Python backend.

From 92fc9791a6bd4f8c9373457796aeb95334f8f99d Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 18 Feb 2026 23:25:50 -0500
Subject: [PATCH 53/56] Revert merge error

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/logger.py           | 2 --
 src/guidellm/scheduler/worker.py | 6 ------
 src/guidellm/settings.py         | 1 -
 3 files changed, 9 deletions(-)

diff --git a/src/guidellm/logger.py b/src/guidellm/logger.py
index 51de22c99..da3464f9d 100644
--- a/src/guidellm/logger.py
+++ b/src/guidellm/logger.py
@@ -15,8 +15,6 @@
         (default: guidellm.log if log file level set else none)
     - GUIDELLM__LOGGING__LOG_FILE_LEVEL: Log level for file logging
         (default: INFO if log file set else none).
-    - GUIDELLM__LOGGING__LOG_BACKEND_EXCEPTIONS: Enable logging of backend
-        exceptions with stacktraces (default: false).
 
 If logging isn't responding to the environment variables, run the `guidellm config`
 command to validate that the environment variables match and are being set correctly.
diff --git a/src/guidellm/scheduler/worker.py b/src/guidellm/scheduler/worker.py
index c49830891..49c18d215 100644
--- a/src/guidellm/scheduler/worker.py
+++ b/src/guidellm/scheduler/worker.py
@@ -38,7 +38,6 @@
 )
 from guidellm.scheduler.strategies import SchedulingStrategy
 from guidellm.schemas import RequestInfo
-from guidellm.settings import settings
 from guidellm.utils import (
     InterProcessMessaging,
     wait_for_sync_barrier,
@@ -388,11 +387,6 @@ async def _process_next_request(self, target_start: float):
                 request_info.error = repr(exc)
                 request_info.traceback = traceback.format_exc()
                 request_info.timings.resolve_end = time.time()
-                # Log backend exception if enabled
-                if settings.logging.log_backend_exceptions:
-                    logger.exception(
-                        f"Backend exception for request {request_info.request_id}: {exc}"
-                    )
                 self._send_update("errored", response, request, request_info)
                 logger.opt(exception=True).debug(
                     f"Backend exception for request {request_info.request_id}"
diff --git a/src/guidellm/settings.py b/src/guidellm/settings.py
index 6b932ed54..81df825e5 100644
--- a/src/guidellm/settings.py
+++ b/src/guidellm/settings.py
@@ -48,7 +48,6 @@ class LoggingSettings(BaseModel):
     console_log_level: str = "WARNING"
     log_file: str | None = None
     log_file_level: str | None = None
-    log_backend_exceptions: bool = False
 
 
 class DatasetSettings(BaseModel):

From b14c4d759ef5d547c918ebe81ae85a3f574efc18 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Wed, 18 Feb 2026 23:27:02 -0500
Subject: [PATCH 54/56] Fix linter errors

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 src/guidellm/backends/openai/http.py                     | 3 +--
 src/guidellm/benchmark/entrypoints.py                    | 1 -
 src/guidellm/benchmark/schemas/generative/entrypoints.py | 2 +-
 3 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/guidellm/backends/openai/http.py b/src/guidellm/backends/openai/http.py
index 6dc3c8b95..6e4eedb27 100644
--- a/src/guidellm/backends/openai/http.py
+++ b/src/guidellm/backends/openai/http.py
@@ -91,8 +91,7 @@ def requires_target(cls) -> bool:
     def requires_model(cls) -> bool:
         """
         OpenAI HTTP backend does not require a model parameter.
-
-        The model can be optional as the backend can use the default model from the server.
+        The model can be optional as we can use the server's default model.
 
         :return: False, as this backend does not require a model parameter
         """
diff --git a/src/guidellm/benchmark/entrypoints.py b/src/guidellm/benchmark/entrypoints.py
index e967d7524..ed89e5c93 100644
--- a/src/guidellm/benchmark/entrypoints.py
+++ b/src/guidellm/benchmark/entrypoints.py
@@ -21,7 +21,6 @@
 
 from guidellm.backends import Backend, BackendType
 from guidellm.benchmark.benchmarker import Benchmarker
-from guidellm.benchmark.schemas.generative.entrypoints import backend_requires_target
 from guidellm.benchmark.outputs import (
     GenerativeBenchmarkerConsole,
     GenerativeBenchmarkerOutput,
diff --git a/src/guidellm/benchmark/schemas/generative/entrypoints.py b/src/guidellm/benchmark/schemas/generative/entrypoints.py
index 9d8f677ce..522d8497f 100644
--- a/src/guidellm/benchmark/schemas/generative/entrypoints.py
+++ b/src/guidellm/benchmark/schemas/generative/entrypoints.py
@@ -42,8 +42,8 @@
 
 __all__ = [
     "BenchmarkGenerativeTextArgs",
-    "backend_requires_target",
     "backend_requires_model",
+    "backend_requires_target",
 ]
 
 

From 32e4af367aa9b8a4477256d2f7cd044a850fe508 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Thu, 19 Feb 2026 20:07:12 -0500
Subject: [PATCH 55/56] Simplify code and address review comments

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 docs/guides/vllm-python-backend.md            |   2 +-
 pyproject.toml                                |   3 +
 src/guidellm/backends/vllm_python/vllm.py     | 148 ++++++------------
 .../backends/vllm_python/vllm_response.py     |  17 +-
 src/guidellm/extras/vllm.py                   |  13 ++
 tests/unit/backends/vllm_python/test_vllm.py  |  88 ++---------
 .../vllm_python/test_vllm_response.py         |  15 ++
 7 files changed, 104 insertions(+), 182 deletions(-)
 create mode 100644 src/guidellm/extras/vllm.py

diff --git a/docs/guides/vllm-python-backend.md b/docs/guides/vllm-python-backend.md
index b7b29074c..1463e3aae 100644
--- a/docs/guides/vllm-python-backend.md
+++ b/docs/guides/vllm-python-backend.md
@@ -29,7 +29,7 @@ Run a benchmark with the vLLM Python backend (no `--target`):
 ```bash
 guidellm benchmark run \
   --backend vllm_python \
-  --model "ibm-granite/granite-4.0-h-350m" \
+  --model "Qwen/Qwen3-0.6B" \
   --data "prompt_tokens=256,output_tokens=128" \
   --max-seconds 20 \
   --rate 3
diff --git a/pyproject.toml b/pyproject.toml
index 20daf9d09..8c6fc5249 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -91,6 +91,9 @@ vision = [
     "datasets[vision]",
     "pillow",
 ]
+vllm = [
+    "vllm",
+]
 # Dev Tooling
 dev = [
     # Install all optional dependencies
diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py
index 7d842c845..9f4a37dce 100644
--- a/src/guidellm/backends/vllm_python/vllm.py
+++ b/src/guidellm/backends/vllm_python/vllm.py
@@ -17,31 +17,29 @@
 from typing import Any, Literal, cast
 
 import numpy as np
-import torch
 
 from guidellm.backends.backend import Backend
 from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler
+from guidellm.extras.vllm import (
+    HAS_VLLM,
+    AsyncEngineArgs,
+    AsyncLLMEngine,
+    RequestOutput,
+    SamplingParams,
+)
 from guidellm.logger import logger
 from guidellm.schemas import (
     GenerationRequest,
     GenerationResponse,
     RequestInfo,
 )
-from guidellm.utils import json
 
 try:
-    from vllm import SamplingParams
-    from vllm.engine.arg_utils import AsyncEngineArgs
-    from vllm.engine.async_llm_engine import AsyncLLMEngine
-    from vllm.outputs import RequestOutput
-
-    HAS_VLLM = True
+    from guidellm.extras.audio import _decode_audio
+    HAS_AUDIO = True
 except ImportError:
-    AsyncLLMEngine = None  # type: ignore[assignment, misc]
-    AsyncEngineArgs = None  # type: ignore[assignment, misc]
-    SamplingParams = None  # type: ignore[assignment, misc]
-    RequestOutput = None  # type: ignore[assignment, misc]
-    HAS_VLLM = False
+    _decode_audio = None  # type: ignore[assignment]
+    HAS_AUDIO = False
 
 _Mode = Literal["audio", "chat", "text"]
 
@@ -199,11 +197,6 @@ def _merge_config(self, user_config: dict[str, Any]) -> dict[str, Any]:
         ):
             config["chat_template"] = self.request_format
 
-        # When CUDA is not available, set device to CPU so vLLM does not rely on
-        # auto-detection (which can yield an empty device_type and raise).
-        if "device" not in config and not torch.cuda.is_available():
-            config["device"] = "cpu"
-
         return config
 
     @property
@@ -404,24 +397,18 @@ def _text_from_output(self, output: RequestOutput | None) -> str:
             return ""
         return output.outputs[0].text or ""
 
-    def _openai_payload_for_mode(
-        self, mode: _Mode, text: str, *, is_delta: bool = False
-    ) -> dict[str, Any]:
+    def _openai_payload_for_mode(self, mode: _Mode, text: str) -> dict[str, Any]:
         """
         Build OpenAI-style payload (content/choices only, no usage or id) for a mode.
 
         :param mode: Inferred mode (audio, chat, text)
-        :param text: Content text (full or delta)
-        :param is_delta: If True, chat uses delta shape; else uses message shape
+        :param text: Content text
         :return: Dict with "text" (audio) or "choices" (text/chat)
         """
         if mode == "audio":
             return {"text": text}
         if mode == "text":
             return {"choices": [{"text": text}]}
-        # chat
-        if is_delta:
-            return {"choices": [{"delta": {"content": text, "role": "assistant"}}]}
         return {"choices": [{"message": {"content": text, "role": "assistant"}}]}
 
     def _stream_usage_tokens(
@@ -542,15 +529,29 @@ def _build_final_response(
         if final_output is None:
             return None
         if stream:
+            # Use provided text (e.g. from cancel) or final vLLM output.
+            final_text = accumulated_text or self._text_from_output(final_output)
             usage = self._usage_from_output(
                 final_output,
                 accumulated_output_tokens=total_output_tokens,
-                accumulated_text=accumulated_text,
+                accumulated_text=final_text,
                 request_info=request_info,
             )
             if usage is not None:
                 response_handler.streaming_usage = usage
-            return response_handler.compile_streaming(request), request_info
+            # We do not send streaming lines during the stream loop, so the handler
+            # never receives an id from the stream. Set it from the final vLLM output
+            # so the response has a usable id.
+            if (
+                response_handler.streaming_response_id is None
+                and hasattr(final_output, "request_id")
+                and final_output.request_id
+            ):
+                response_handler.streaming_response_id = final_output.request_id
+            # Build response with final text only (no streamed chunks).
+            return response_handler.compile_streaming(
+                request, text_override=final_text
+            ), request_info
         openai_format = self._convert_vllm_output_to_openai_format(
             final_output, ctx.mode
         )
@@ -801,7 +802,7 @@ def _convert_vllm_output_to_openai_format(
         usage = self._usage_from_output(output) or {}
 
         response_dict: dict[str, Any] = {
-            **self._openai_payload_for_mode(mode, generated_text, is_delta=False),
+            **self._openai_payload_for_mode(mode, generated_text),
             "usage": usage,
         }
         if hasattr(output, "request_id") and output.request_id:
@@ -820,13 +821,11 @@ def _build_audio_generate_input(
             )
         if len(audio_bytes) == 0:
             raise ValueError("Audio data cannot be empty")
-        try:
-            from guidellm.extras.audio import _decode_audio
-        except ImportError as e:
+        if not HAS_AUDIO or _decode_audio is None:
             raise ImportError(
                 "Audio support requires guidellm[audio] (torchcodec). "
                 "Install with: pip install 'guidellm[audio]'"
-            ) from e
+            )
         try:
             audio_samples = _decode_audio(audio_bytes)
             if hasattr(audio_samples.data, "numpy"):
@@ -844,52 +843,6 @@ def _build_audio_generate_input(
                 f"Failed to decode audio data for vLLM: {exc}"
             ) from exc
 
-    def _process_streaming_delta(
-        self,
-        request_output: RequestOutput,
-        ctx: _RequestContext,
-        response_handler: VLLMResponseHandler,
-        request_info: RequestInfo,
-        accumulated_text: str,
-        previous_token_count: int,
-        iter_time: float,
-    ) -> tuple[str, int, int]:
-        """Emit one streaming delta; return (accum_text, token_delta, new_prev)."""
-        generated_text = self._text_from_output(request_output)
-        if generated_text == accumulated_text:
-            return (accumulated_text, 0, previous_token_count)
-
-        delta_text = generated_text[len(accumulated_text) :]
-        delta_data = self._openai_payload_for_mode(
-            ctx.mode, delta_text, is_delta=True
-        )
-        if hasattr(request_output, "request_id") and request_output.request_id:
-            delta_data["id"] = request_output.request_id
-        json_str = json.dumps(delta_data)
-        json_str_decoded = (
-            json_str.decode("utf-8") if isinstance(json_str, bytes) else json_str
-        )
-        sse_line = f"data: {json_str_decoded}"
-        iterations = response_handler.add_streaming_line(sse_line)
-
-        total_delta = 0
-        new_prev = previous_token_count
-        if iterations is not None and iterations > 0:
-            out = (
-                request_output.outputs[0]
-                if request_output.outputs
-                else None
-            )
-            if out is not None and out.token_ids is not None:
-                current_count = len(out.token_ids)
-                total_delta = max(0, current_count - previous_token_count)
-                new_prev = current_count
-            else:
-                total_delta = 1
-                new_prev = previous_token_count + 1
-            self._update_token_timing(request_info, iter_time, iterations)
-        return (generated_text, total_delta, new_prev)
-
     def _raise_generation_error(self, exc: BaseException) -> None:
         """Re-raise generation failure with context; special case for audio."""
         error_msg = str(exc)
@@ -916,10 +869,7 @@ async def _run_generation(
         state: dict[str, Any],
     ) -> AsyncIterator[tuple[GenerationResponse, RequestInfo]]:
         """Run engine.generate loop and yield final (response, request_info)."""
-        state["final_output"] = None
-        state["accumulated_text"] = ""
-        state["total_output_tokens"] = 0
-        previous_token_count = 0
+        state["final_output"] = None  # Only state we keep; text/usage from final output
         stream = ctx.stream
         engine = self._engine
         if engine is None:
@@ -941,30 +891,22 @@ async def _run_generation(
                 break
 
             if stream:
-                state["accumulated_text"], token_delta, previous_token_count = (
-                    self._process_streaming_delta(
-                        request_output,
-                        ctx,
-                        response_handler,
-                        request_info,
-                        state["accumulated_text"],
-                        previous_token_count,
-                        iter_time,
-                    )
-                )
-                if token_delta > 0:
-                    state["total_output_tokens"] += token_delta
+                # The main advantage of streaming is more granular timing information
+                # can be saved here.
+                self._update_token_timing(request_info, iter_time, 1)
 
         request_info.timings.request_end = time.time()
+        final_output = state["final_output"]
+        # Text and usage from final output only; pass 0 for accumulated tokens.
         result = self._build_final_response(
             request,
             request_info,
-            state["final_output"],
+            final_output,
             ctx,
             response_handler,
             stream,
-            state["accumulated_text"],
-            state["total_output_tokens"],
+            self._text_from_output(final_output) if final_output else "",
+            0,
         )
         if result is not None:
             yield result
@@ -1032,15 +974,17 @@ async def resolve(  # type: ignore[override, misc]
             ):
                 yield result
         except asyncio.CancelledError as err:
+            # Yield whatever we have so far (same pattern: text from output, 0 tokens).
+            final_output = gen_state.get("final_output")
             cancel_result = self._build_final_response(
                 request,
                 request_info,
-                gen_state.get("final_output"),
+                final_output,
                 ctx,
                 response_handler,
                 ctx.stream,
-                gen_state.get("accumulated_text", ""),
-                gen_state.get("total_output_tokens", 0),
+                self._text_from_output(final_output) if final_output else "",
+                0,
             )
             if cancel_result is not None:
                 yield cancel_result
diff --git a/src/guidellm/backends/vllm_python/vllm_response.py b/src/guidellm/backends/vllm_python/vllm_response.py
index 624193e65..792f22cd3 100644
--- a/src/guidellm/backends/vllm_python/vllm_response.py
+++ b/src/guidellm/backends/vllm_python/vllm_response.py
@@ -99,14 +99,25 @@ def add_streaming_line(self, line: str) -> int | None:
                         result = 1
         return result
 
-    def compile_streaming(self, request: GenerationRequest) -> GenerationResponse:
+    def compile_streaming(
+        self,
+        request: GenerationRequest,
+        *,
+        text_override: str | None = None,
+    ) -> GenerationResponse:
         """
         Compile accumulated streaming chunks into a final GenerationResponse.
 
         :param request: Original generation request
-        :return: GenerationResponse with concatenated text and metrics
+        :param text_override: If provided, use as response text instead of
+            joining streaming_texts (e.g. when backend supplies final text only).
+        :return: GenerationResponse with text and metrics
         """
-        text = "".join(self.streaming_texts)
+        text = (
+            text_override
+            if text_override is not None
+            else "".join(self.streaming_texts)
+        )
         input_metrics, output_metrics = self._extract_metrics(
             self.streaming_usage, text
         )
diff --git a/src/guidellm/extras/vllm.py b/src/guidellm/extras/vllm.py
new file mode 100644
index 000000000..a415e966f
--- /dev/null
+++ b/src/guidellm/extras/vllm.py
@@ -0,0 +1,13 @@
+try:
+    from vllm import SamplingParams
+    from vllm.engine.arg_utils import AsyncEngineArgs
+    from vllm.engine.async_llm_engine import AsyncLLMEngine
+    from vllm.outputs import RequestOutput
+
+    HAS_VLLM = True
+except ImportError:
+    AsyncLLMEngine = None  # type: ignore[assignment, misc]
+    AsyncEngineArgs = None  # type: ignore[assignment, misc]
+    SamplingParams = None  # type: ignore[assignment, misc]
+    RequestOutput = None  # type: ignore[assignment, misc]
+    HAS_VLLM = False
diff --git a/tests/unit/backends/vllm_python/test_vllm.py b/tests/unit/backends/vllm_python/test_vllm.py
index aa664b288..1d8db29fb 100644
--- a/tests/unit/backends/vllm_python/test_vllm.py
+++ b/tests/unit/backends/vllm_python/test_vllm.py
@@ -369,72 +369,6 @@ def test_vllm_config_empty_uses_vllm_defaults(self):
             assert "gpu_memory_utilization" not in b.vllm_config
 
 
-class TestVLLMMergeConfigDevice:
-    """
-    Test _merge_config device logic: default to CPU when CUDA is unavailable.
-    ## WRITTEN BY AI ##
-    """
-
-    @pytest.mark.smoke
-    def test_cuda_unavailable_no_device_sets_cpu(self):
-        """
-        When torch.cuda.is_available() is False and user does not set device,
-        merged config includes device="cpu".
-        ## WRITTEN BY AI ##
-        """
-        with (
-            patch("guidellm.backends.vllm_python.vllm._check_vllm_available"),
-            patch(
-                "guidellm.backends.vllm_python.vllm.torch.cuda.is_available",
-                return_value=False,
-            ),
-        ):
-            backend = VLLMPythonBackend(model="test-model")
-        assert backend.vllm_config.get("device") == "cpu"
-        assert backend.vllm_config.get("model") == "test-model"
-
-    @pytest.mark.smoke
-    def test_cuda_unavailable_user_sets_device_preserved(self):
-        """
-        When torch.cuda.is_available() is False and user sets device,
-        merged config keeps the user's device (no override).
-        ## WRITTEN BY AI ##
-        """
-        with (
-            patch("guidellm.backends.vllm_python.vllm._check_vllm_available"),
-            patch(
-                "guidellm.backends.vllm_python.vllm.torch.cuda.is_available",
-                return_value=False,
-            ),
-        ):
-            backend_cpu = VLLMPythonBackend(
-                model="test-model", vllm_config={"device": "cpu"}
-            )
-            backend_cuda = VLLMPythonBackend(
-                model="test-model", vllm_config={"device": "cuda"}
-            )
-        assert backend_cpu.vllm_config.get("device") == "cpu"
-        assert backend_cuda.vllm_config.get("device") == "cuda"
-
-    @pytest.mark.smoke
-    def test_cuda_available_no_device_not_added(self):
-        """
-        When torch.cuda.is_available() is True and user does not set device,
-        merged config does not add device (vLLM auto-detection is used).
-        ## WRITTEN BY AI ##
-        """
-        with (
-            patch("guidellm.backends.vllm_python.vllm._check_vllm_available"),
-            patch(
-                "guidellm.backends.vllm_python.vllm.torch.cuda.is_available",
-                return_value=True,
-            ),
-        ):
-            backend = VLLMPythonBackend(model="test-model")
-        assert "device" not in backend.vllm_config
-        assert backend.vllm_config.get("model") == "test-model"
-
-
 class TestVLLMExtractPromptTokenizerWarnings:
     """
     Test warnings when tokenizer is set but not used for prompt extraction.
@@ -962,7 +896,7 @@ def test_openai_payload_audio_returns_text_key(self, backend):
         mode audio -> {"text": ...}.
         ## WRITTEN BY AI ##
         """
-        out = backend._openai_payload_for_mode("audio", "transcribed", is_delta=False)
+        out = backend._openai_payload_for_mode("audio", "transcribed")
         assert out == {"text": "transcribed"}
 
     @pytest.mark.smoke
@@ -971,25 +905,27 @@ def test_openai_payload_text_returns_choices_text(self, backend):
         mode text -> choices[].text.
         ## WRITTEN BY AI ##
         """
-        out = backend._openai_payload_for_mode("text", "completion", is_delta=False)
+        out = backend._openai_payload_for_mode("text", "completion")
         assert out == {"choices": [{"text": "completion"}]}
 
     @pytest.mark.smoke
-    def test_openai_payload_chat_delta_returns_choices_delta(self, backend):
+    def test_openai_payload_chat_returns_choices_message(self, backend):
         """
-        mode chat is_delta=True -> choices[].delta.
+        mode chat -> choices[].message (no delta shape; message only).
         ## WRITTEN BY AI ##
         """
-        out = backend._openai_payload_for_mode("chat", "hi", is_delta=True)
-        assert out == {"choices": [{"delta": {"content": "hi", "role": "assistant"}}]}
+        out = backend._openai_payload_for_mode("chat", "hi")
+        assert out == {
+            "choices": [{"message": {"content": "hi", "role": "assistant"}}]
+        }
 
     @pytest.mark.smoke
     def test_openai_payload_chat_message_returns_choices_message(self, backend):
         """
-        mode chat is_delta=False -> choices[].message.
+        mode chat -> choices[].message.
         ## WRITTEN BY AI ##
         """
-        out = backend._openai_payload_for_mode("chat", "hello", is_delta=False)
+        out = backend._openai_payload_for_mode("chat", "hello")
         assert out == {
             "choices": [{"message": {"content": "hello", "role": "assistant"}}]
         }
@@ -1460,7 +1396,7 @@ async def mock_generate_raise(*args, **kwargs):
             raise ValueError("At most 0 audio(s) may be provided.")
             yield  # makes this an async generator so async for raises
 
-        with patch("guidellm.extras.audio._decode_audio") as mock_decode:
+        with patch("guidellm.backends.vllm_python.vllm._decode_audio") as mock_decode:
             mock_decode.return_value = Mock(data=np.array([0.0]))
             backend._engine = Mock()
             backend._engine.generate = mock_generate_raise
@@ -1513,7 +1449,7 @@ async def mock_generate(prompt, sampling_params, request_id):
             yield req_out
 
         with patch(
-            "guidellm.extras.audio._decode_audio",
+            "guidellm.backends.vllm_python.vllm._decode_audio",
             return_value=mock_decode_result,
         ):
             backend._engine = Mock()
diff --git a/tests/unit/backends/vllm_python/test_vllm_response.py b/tests/unit/backends/vllm_python/test_vllm_response.py
index 8240c6669..79af25d75 100644
--- a/tests/unit/backends/vllm_python/test_vllm_response.py
+++ b/tests/unit/backends/vllm_python/test_vllm_response.py
@@ -185,6 +185,21 @@ def test_compile_streaming_uses_accumulated_text(
         assert out.text == "Hello world"
         assert out.request_id == request_without_arguments.request_id
 
+    @pytest.mark.smoke
+    def test_compile_streaming_text_override_uses_given_text(
+        self, handler, request_without_arguments
+    ):
+        """
+        compile_streaming with text_override uses that text instead of streaming_texts.
+        ## WRITTEN BY AI ##
+        """
+        handler.add_streaming_line('data: {"choices": [{"text": "ignored"}]}')
+        out = handler.compile_streaming(
+            request_without_arguments, text_override="final text"
+        )
+        assert out.text == "final text"
+        assert out.request_id == request_without_arguments.request_id
+
     @pytest.mark.smoke
     def test_compile_streaming_stores_response_id(
         self, handler, request_without_arguments

From ad0015a71c267cf17e6ce15a5062f60ae69e7df7 Mon Sep 17 00:00:00 2001
From: Jared O'Connell <joconnel@redhat.com>
Date: Mon, 23 Feb 2026 17:43:30 -0500
Subject: [PATCH 56/56] Update lockfile

Signed-off-by: Jared O'Connell <joconnel@redhat.com>
---
 uv.lock | 2671 ++++++++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 2562 insertions(+), 109 deletions(-)

diff --git a/uv.lock b/uv.lock
index 4fd7b1bb2..de2f48723 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,9 +2,13 @@ version = 1
 revision = 3
 requires-python = ">=3.10.0, <4.0"
 resolution-markers = [
-    "python_full_version >= '3.12' and sys_platform != 'darwin'",
+    "python_full_version >= '3.14' and sys_platform != 'darwin'",
+    "python_full_version == '3.13.*' and sys_platform != 'darwin'",
+    "python_full_version == '3.12.*' and sys_platform != 'darwin'",
     "python_full_version == '3.11.*' and sys_platform != 'darwin'",
-    "python_full_version >= '3.12' and sys_platform == 'darwin'",
+    "python_full_version >= '3.14' and sys_platform == 'darwin'",
+    "python_full_version == '3.13.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and sys_platform == 'darwin'",
     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
     "python_full_version < '3.11' and sys_platform != 'darwin'",
     "python_full_version < '3.11' and sys_platform == 'darwin'",
@@ -184,6 +188,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl", hash = "sha256:b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92", size = 13511, upload-time = "2024-01-10T00:56:08.388Z" },
 ]
 
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -193,6 +206,25 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
 ]
 
+[[package]]
+name = "anthropic"
+version = "0.83.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "distro" },
+    { name = "docstring-parser" },
+    { name = "httpx" },
+    { name = "jiter" },
+    { name = "pydantic" },
+    { name = "sniffio" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/db/e5/02cd2919ec327b24234abb73082e6ab84c451182cc3cc60681af700f4c63/anthropic-0.83.0.tar.gz", hash = "sha256:a8732c68b41869266c3034541a31a29d8be0f8cd0a714f9edce3128b351eceb4", size = 534058, upload-time = "2026-02-19T19:26:38.904Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5f/75/b9d58e4e2a4b1fc3e75ffbab978f999baf8b7c4ba9f96e60edb918ba386b/anthropic-0.83.0-py3-none-any.whl", hash = "sha256:f069ef508c73b8f9152e8850830d92bd5ef185645dbacf234bb213344a274810", size = 456991, upload-time = "2026-02-19T19:26:40.114Z" },
+]
+
 [[package]]
 name = "anyio"
 version = "4.12.0"
@@ -207,6 +239,50 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb", size = 113362, upload-time = "2025-11-28T23:36:57.897Z" },
 ]
 
+[[package]]
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 source = { editable = "." }
@@ -803,8 +1631,7 @@ dependencies = [
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 [package.dev-dependencies]
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 [package.metadata.requires-dev]
 dev = [{ name = "guidellm", extras = ["dev"] }]
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