README.md

---
base_model: meta-llama/Llama-2-7b-chat-hf
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-2-7b-chat-hf
- lora
- transformers
license: cc-by-nc-4.0
---

# Reson — LLaMA-2 7B LoRA Fine-Tune

⚠️ Note: Reson does **not hallucinate** in the usual sense.  
It was trained to **adapt** — outputs may look unconventional or speculative because the objective is **meta-cognition and adaptive strategy**, not strict factual recall.

Reson is a LoRA fine-tuned version of **LLaMA-2 7B Chat**, trained on ~11k instruction/response pairs.  
It simulates **reflective and strategic thinking** across multiple domains.

---

## Model Details

### Model Description
- **What it is:** LoRA adapters for LLaMA-2 7B Chat focused on *adaptive reasoning under uncertainty*.  
- **Why:** To explore identity emergence, strategic simulation, cross-domain transfer, and explicit self-reflection.  
- **How it behaves:** Outputs may appear “hallucinatory” but are actually *adaptive responses* guided by meta-cognition.

- **Developed by:** Nexus-Walker (Daniele Cangi)  
- **Model type:** Causal LM (PEFT/LoRA adapters)  
- **Languages:** English, Italian  
- **License:** Business Source License (BSL 1.1)  
- **Finetuned from model:** `meta-llama/Llama-2-7b-chat-hf`

### Model Sources
- **Repository:** https://huggingface.co/Nexus-Walker/Reson  
- **Demo transcripts:** [`demo_chat.md`](./demo_chat.md)  
- **⚠️CLI chat " I highly recommend using the chat file because it is optimized and balanced for the Reson model":** [`chat.py`](./chat.py)

---

## Uses

### Direct Use
- Research on **meta-cognition** and **adaptive reasoning** in LLMs.  
- Creative simulations across domains (business strategy, adversarial contexts, scientific discussion).  
- Conversational demos exploring identity, reflection, and scenario planning.

### Downstream Use
- Integration into **decision-support pipelines**.  
- **Multi-agent experiments** with reflective/strategic agents.

### Out-of-Scope Use
- Benchmark-style factual QA.  
- Critical applications (medical, legal, safety).

---

## Bias, Risks, and Limitations
- Optimized for **adaptation**, not factual accuracy.  
- May generate speculative narratives by design.  
- Not suitable for unsupervised high-stakes use.

### Recommendations
- Treat outputs as **reasoning simulations**.  
- Always apply **human-in-the-loop** in sensitive contexts.

---

## How to Get Started

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base = "meta-llama/Llama-2-7b-chat-hf"
adapter = "Nexus-Walker/Reson"

tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto", load_in_4bit=True)
model = PeftModel.from_pretrained(model, adapter)

prompt = "Who are you?"
inputs = tok(prompt, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=150)
print(tok.decode(out[0], skip_special_tokens=True))