](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T)
-
-Try it out via this [demo](https://bitnet-demo.azurewebsites.net/), or build and run it on your own [CPU](https://github.com/microsoft/BitNet?tab=readme-ov-file#build-from-source) or [GPU](https://github.com/microsoft/BitNet/blob/main/gpu/README.md).
-
-bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels, that support **fast** and **lossless** inference of 1.58-bit models on CPU and GPU (NPU support will coming next).
-
-The first release of bitnet.cpp is to support inference on CPUs. bitnet.cpp achieves speedups of **1.37x** to **5.07x** on ARM CPUs, with larger models experiencing greater performance gains. Additionally, it reduces energy consumption by **55.4%** to **70.0%**, further boosting overall efficiency. On x86 CPUs, speedups range from **2.37x** to **6.17x** with energy reductions between **71.9%** to **82.2%**. Furthermore, bitnet.cpp can run a 100B BitNet b1.58 model on a single CPU, achieving speeds comparable to human reading (5-7 tokens per second), significantly enhancing the potential for running LLMs on local devices. Please refer to the [technical report](https://arxiv.org/abs/2410.16144) for more details.
-
-
-
-
->The tested models are dummy setups used in a research context to demonstrate the inference performance of bitnet.cpp.
-
-## Demo
-
-A demo of bitnet.cpp running a BitNet b1.58 3B model on Apple M2:
-
-https://github.com/user-attachments/assets/7f46b736-edec-4828-b809-4be780a3e5b1
-
-## What's New:
-- 05/20/2025 [BitNet Official GPU inference kernel](https://github.com/microsoft/BitNet/blob/main/gpu/README.md) 
-- 04/14/2025 [BitNet Official 2B Parameter Model on Hugging Face](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T)
-- 02/18/2025 [Bitnet.cpp: Efficient Edge Inference for Ternary LLMs](https://arxiv.org/abs/2502.11880)
-- 11/08/2024 [BitNet a4.8: 4-bit Activations for 1-bit LLMs](https://arxiv.org/abs/2411.04965)
-- 10/21/2024 [1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs](https://arxiv.org/abs/2410.16144)
-- 10/17/2024 bitnet.cpp 1.0 released.
-- 03/21/2024 [The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ](https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf)
-- 02/27/2024 [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://arxiv.org/abs/2402.17764)
-- 10/17/2023 [BitNet: Scaling 1-bit Transformers for Large Language Models](https://arxiv.org/abs/2310.11453)
-
-## Acknowledgements
-
-This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp) framework. We would like to thank all the authors for their contributions to the open-source community. Also, bitnet.cpp's kernels are built on top of the Lookup Table methodologies pioneered in [T-MAC](https://github.com/microsoft/T-MAC/). For inference of general low-bit LLMs beyond ternary models, we recommend using T-MAC.
-## Official Models
-| Model | -Parameters | -CPU | -Kernel | -||
|---|---|---|---|---|---|
| I2_S | -TL1 | -TL2 | -|||
| BitNet-b1.58-2B-4T | -2.4B | -x86 | -✅ | -❌ | -✅ | -
| ARM | -✅ | -✅ | -❌ | -||
| Model | -Parameters | -CPU | -Kernel | -||
|---|---|---|---|---|---|
| I2_S | -TL1 | -TL2 | -|||
| bitnet_b1_58-large | -0.7B | -x86 | -✅ | -❌ | -✅ | -
| ARM | -✅ | -✅ | -❌ | -||
| bitnet_b1_58-3B | -3.3B | -x86 | -❌ | -❌ | -✅ | -
| ARM | -❌ | -✅ | -❌ | -||
| Llama3-8B-1.58-100B-tokens | -8.0B | -x86 | -✅ | -❌ | -✅ | -
| ARM | -✅ | -✅ | -❌ | -||
| Falcon3 Family | -1B-10B | -x86 | -✅ | -❌ | -✅ | -
| ARM | -✅ | -✅ | -❌ | -||
| Falcon-E Family | -1B-3B | -x86 | -✅ | -❌ | -✅ | -
| ARM | -✅ | -✅ | -❌ | -||