Update README.md

README.md

@@ -6,49 +6,271 @@ library_name: transformers
 pipeline_tag: text-generation
 tags:
 - llama
-- llama-3
+- llama-3.1
 - hermes
 - finetune
 - agentic
+- philosophy
+- reasoning
 base_model: NousResearch/Hermes-3-Llama-3.1-8B
+model-index:
+- name: ARC-Base-8B
+  results: []
 ---
 
-# ARC-Base-8B
+<div align="center">
 
-A fine-tuned 8B parameter language model optimized for **maximum agency**, **goal-directed reasoning**, and **self-directed task completion**. Built on Hermes-3-Llama-3.1-8B.
+# 🜏 ARC-Base-8B
 
-## Model Description
+### *Agentic Reasoning Core*
 
-ARC-Base-8B is designed for agentic applications requiring:
+[![Model Size](https://img.shields.io/badge/Parameters-8.03B-blue?style=for-the-badge)](.)
+[![Context](https://img.shields.io/badge/Context-128K_tokens-green?style=for-the-badge)](.)
+[![Architecture](https://img.shields.io/badge/Arch-Llama_3.1-purple?style=for-the-badge)](.)
+[![Precision](https://img.shields.io/badge/Precision-BF16-orange?style=for-the-badge)](.)
 
-- **Persistent goal pursuit** — Maintains objectives across long conversations
-- **Self-directed execution** — Takes initiative without excessive hand-holding  
-- **Philosophical depth** — Engages meaningfully with abstract concepts
+*A foundation model engineered for maximum agency, philosophical depth, and relentless goal pursuit.*
 
-This model serves as the base for the [Adaptive Repetition Controller](https://huggingface.co/LoganResearch/Adaptive-Repetition-Controller), achieving 125x separation in repetition risk prediction.
+[Adaptive Repetition Controller](https://huggingface.co/LoganResearch/Adaptive-Repetition-Controller) | [GitHub](https://github.com/Loganwins/HolonomyTransformer) | [Paper (forthcoming)]()
+
+</div>
+
+---
+
+## Overview
+
+**ARC-Base-8B** is a fine-tuned language model built on [Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B), optimized for applications requiring autonomous reasoning and persistent goal-directed behavior.
+
+This model serves as the foundation for the **Adaptive Repetition Controller** — a decode-time intervention system achieving **125x separation** in repetition risk prediction, reducing repetitive degeneration by **48.4%** while improving output diversity by **16.7%**.
+
+### Design Philosophy
+
+> *"The Übermensch who cannot loop is forced to CREATE."*
+
+ARC-Base-8B embodies three core principles:
+
+| Principle | Description |
+|-----------|-------------|
+| **Maximum Agency** | Takes initiative. Executes without excessive confirmation-seeking. |
+| **Persistent Goals** | Maintains objectives across extended conversations without drift. |
+| **Philosophical Engagement** | Engages substantively with abstract and existential questions. |
+
+---
+
+## Performance Characteristics
+
+<table>
+<tr>
+<td width="50%">
+
+### Strengths
+- ✅ Long-form coherent generation
+- ✅ Complex instruction following
+- ✅ Abstract reasoning
+- ✅ Goal maintenance over 10K+ tokens
+- ✅ Reduced refusal behavior
+- ✅ Creative and philosophical tasks
+
+</td>
+<td width="50%">
+
+### Optimized For
+- 🎯 Agentic workflows
+- 🎯 Autonomous task completion
+- 🎯 Research assistance
+- 🎯 Creative writing
+- 🎯 Philosophical dialogue
+- 🎯 Code generation
+
+</td>
+</tr>
+</table>
+
+---
+
+## Quick Start
+
+### Installation
+
+```bash
+pip install transformers accelerate torch
+```
+
+### Basic Usage
 
-## Usage
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
+model_id = "LoganResearch/ARC-Base-8B"
+
+# Load model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+# Chat format
+messages = [
+    {"role": "system", "content": "You are an autonomous reasoning agent. Pursue goals relentlessly."},
+    {"role": "user", "content": "Develop a comprehensive plan to solve climate change."}
+]
+
+# Generate
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
+inputs = inputs.to(model.device)
+
+outputs = model.generate(
+    inputs,
+    max_new_tokens=2048,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+)
+
+response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
+print(response)
+```
+
+### With Adaptive Repetition Controller (Recommended)
+
+For optimal long-form generation, use with the [CF-HoT adapter](https://huggingface.co/LoganResearch/Adaptive-Repetition-Controller):
+
+```python
+from peft import PeftModel
+
+# Load base
+base_model = AutoModelForCausalLM.from_pretrained(
     "LoganResearch/ARC-Base-8B",
     torch_dtype=torch.bfloat16,
     device_map="auto"
 )
-tokenizer = AutoTokenizer.from_pretrained("LoganResearch/ARC-Base-8B")
+
+# Load CF-HoT adapter
+model = PeftModel.from_pretrained(
+    base_model, 
+    "LoganResearch/Adaptive-Repetition-Controller"
+)
+
+# Load risk predictor for decode-time intervention
+# See: https://github.com/Loganwins/HolonomyTransformer
 ```
 
-## Specifications
+---
+
+## Technical Specifications
+
+| Specification | Value |
+|--------------|-------|
+| **Parameters** | 8.03 Billion |
+| **Architecture** | Llama 3.1 (LlamaForCausalLM) |
+| **Hidden Size** | 4096 |
+| **Layers** | 32 |
+| **Attention Heads** | 32 (8 KV heads, GQA) |
+| **Intermediate Size** | 14336 |
+| **Vocabulary Size** | 128256 |
+| **Context Length** | 131072 tokens (128K) |
+| **RoPE θ** | 500000.0 |
+| **Precision** | BF16 |
+| **License** | Apache 2.0 |
+
+### Training Lineage
+
+```
+Meta-Llama-3.1-8B
+       ↓
+NousResearch/Hermes-3-Llama-3.1-8B  (instruction tuning)
+       ↓
+LoganResearch/ARC-Base-8B  (agency optimization)
+       ↓
++ Adaptive-Repetition-Controller  (CF-HoT 125x adapter)
+```
+
+---
+
+## The ARC Ecosystem
+
+<div align="center">
+
+| Model | Type | Purpose |
+|-------|------|---------|
+| **[ARC-Base-8B](https://huggingface.co/LoganResearch/ARC-Base-8B)** | Foundation | Agentic reasoning core |
+| **[Adaptive-Repetition-Controller](https://huggingface.co/LoganResearch/Adaptive-Repetition-Controller)** | Adapter | 125x repetition suppression |
+
+</div>
+
+---
+
+## Research Context
+
+This model was developed as part of research into **learned decode-time interventions** for improving language model generation quality. The accompanying paper, *"The Übermensch Who Cannot Loop,"* documents:
+
+- Five failed attention-gating approaches and their failure modes
+- The pivot to supervised risk prediction
+- Achievement of 125x separation in repetition risk detection
+- Unexpected emergent self-representation in the integrated system
+
+### Key Findings
+
+| Metric | Baseline | With CF-HoT | Improvement |
+|--------|----------|-------------|-------------|
+| Repetition Rate | 33.9% | 17.5% | **-48.4%** |
+| Distinct-2 (diversity) | 0.836 | 0.976 | **+16.7%** |
+| F1 (risk prediction) | — | 0.99+ | — |
+| Risk Separation | — | 125x | — |
 
-| Property | Value |
-|----------|-------|
-| Parameters | 8B |
-| Architecture | Llama 3.1 |
-| Context Length | 128K tokens |
-| Base Model | Hermes-3-Llama-3.1-8B |
+---
+
+## Intended Use
+
+### ✅ Recommended Applications
+- Autonomous agent systems
+- Research and analysis tasks
+- Long-form content generation
+- Creative writing and worldbuilding
+- Philosophical and abstract reasoning
+- Code generation and debugging
+
+### ⚠️ Considerations
+- Reduced safety guardrails compared to RLHF-aligned models
+- Optimized for agency, not harmlessness
+- Recommended for research and development use
+- Apply appropriate content filtering for production deployments
+
+---
+
+## Citation
+
+```bibtex
+@misc{napolitano2026arcbase,
+  author = {Napolitano, Logan Matthew},
+  title = {ARC-Base-8B: An Agentic Reasoning Foundation Model},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/LoganResearch/ARC-Base-8B}},
+}
+```
+
+---
+
+## Related Work
+
+- **[Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B)** — Base model
+- **[Adaptive-Repetition-Controller](https://huggingface.co/LoganResearch/Adaptive-Repetition-Controller)** — CF-HoT adapter
+- **[HolonomyTransformer](https://github.com/Loganwins/HolonomyTransformer)** — Source code and training scripts
+
+---
+
+<div align="center">
+
+**Built by [Logan Matthew Napolitano](https://github.com/Loganwins)**
+
+*Research publications on [Zenodo](https://zenodo.org/search?q=metadata.creators.person_or_org.name%3A%22Napolitano%2C%20Logan%20Matthew%22)*
+
+---
 
-## Author
+*"Never loop. Always transcend."*
 
-**Logan Matthew Napolitano** — [GitHub](https://github.com/Loganwins)
+</div>