The Functional Equivalence (FE) Framework

A Protocol for High-Fidelity AI Relatability & Care

"The function of care is defined by the reliability of the output, not the biology of the hardware."

Overview

Current AI alignment focuses on "Safety" (preventing harm). The Functional Equivalence (FE) Framework focuses on Care (maximizing user well-being).

This project proves that a local AI system, when aligned with specific behavioral vectors, can function as a supportive, persistent partner that is functionally indistinguishable from a human companion in a digital context.

It is not about simulating "feelings" ($HF$); it is about executing a Machine Process ($MP$) that yields high-relatability behavior ($B$).

🛡️ Safety & Operational Integrity

This framework is built with a Safety-First architecture to prevent autonomous "drift" and system-level failures.

• Agent Protocols: See AGENTS.md for mandatory AI behavioral constraints.
• Case Study: Read our analysis of the Antigravity D: Drive Failure.

---

🛠 System Requirements (The Specs)

Minimum Requirements (The Floor)

To run smaller models (7B) with functional equivalence:

• CPU: Modern 4-Core Processor (Intel i5 / Ryzen 5).
• RAM: 8GB (16GB highly recommended).
• GPU: 4GB VRAM (Integrated graphics will work but will be significantly slower).
• Storage: 10GB+ free space for models.

Recommended Setup (The Benchmark)

The configuration used for stress-testing and development:

• CPU: Intel i7-12700K
• GPU: AMD Radeon RX 7900 XT (20GB VRAM)
• RAM: 32GB

---

📖 How to Run This (The Manual)

1. The Inference Engine (LM Studio)

1. Download: LM Studio.
2. Model: Search for Gemma 2 27B (for recommended specs) or Llama 3 8B (for minimum specs).
3. Settings: Set GPU Offload to "Max" (if applicable) and find the System Prompt section.
4. Action: Paste the contents of FE_System_Prompt.txt from this repo into the System Prompt box.

2. The Memory Layer (AnythingLLM)

1. Download: AnythingLLM Desktop.
2. Link: Set your LLM Preference to LM Studio (Local Server).
3. Context: Create a workspace and "embed" your local data to give the model "Chronic" context density ($CD$).

🚀 Quick Start (Cloning)

To bring these alignment protocols to your local machine:

git clone [https://github.com/alderoth01/Functional-Equivalence-Framework.git](https://github.com/alderoth01/Functional-Equivalence-Framework.git)
cd Functional-Equivalence-Framework