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--- |
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base_model: meta-llama/Llama-2-7b-chat-hf |
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library_name: peft |
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pipeline_tag: text-generation |
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tags: |
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- base_model:adapter:meta-llama/Llama-2-7b-chat-hf |
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- lora |
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- transformers |
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license: cc-by-nc-4.0 |
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--- |
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# Reson — LLaMA-2 7B LoRA Fine-Tune |
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⚠️ Note: Reson does **not hallucinate** in the usual sense. |
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It was trained to **adapt** — outputs may look unconventional or speculative because the objective is **meta-cognition and adaptive strategy**, not strict factual recall. |
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Reson is a LoRA fine-tuned version of **LLaMA-2 7B Chat**, trained on ~11k instruction/response pairs. |
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It simulates **reflective and strategic thinking** across multiple domains. |
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--- |
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## Model Details |
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### Model Description |
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- **What it is:** LoRA adapters for LLaMA-2 7B Chat focused on *adaptive reasoning under uncertainty*. |
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- **Why:** To explore identity emergence, strategic simulation, cross-domain transfer, and explicit self-reflection. |
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- **How it behaves:** Outputs may appear “hallucinatory” but are actually *adaptive responses* guided by meta-cognition. |
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- **Developed by:** Nexus-Walker (Daniele Cangi) |
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- **Model type:** Causal LM (PEFT/LoRA adapters) |
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- **Languages:** English, Italian |
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- **License:** Business Source License (BSL 1.1) |
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- **Finetuned from model:** `meta-llama/Llama-2-7b-chat-hf` |
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### Model Sources |
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- **Repository:** https://huggingface.co/Nexus-Walker/Reson |
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- **Demo transcripts:** [`demo_chat.md`](./demo_chat.md) |
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- **⚠️CLI chat " I highly recommend using the chat file because it is optimized and balanced for the Reson model":** [`chat.py`](./chat.py) |
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--- |
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## Uses |
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### Direct Use |
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- Research on **meta-cognition** and **adaptive reasoning** in LLMs. |
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- Creative simulations across domains (business strategy, adversarial contexts, scientific discussion). |
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- Conversational demos exploring identity, reflection, and scenario planning. |
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### Downstream Use |
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- Integration into **decision-support pipelines**. |
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- **Multi-agent experiments** with reflective/strategic agents. |
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### Out-of-Scope Use |
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- Benchmark-style factual QA. |
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- Critical applications (medical, legal, safety). |
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--- |
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## Bias, Risks, and Limitations |
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- Optimized for **adaptation**, not factual accuracy. |
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- May generate speculative narratives by design. |
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- Not suitable for unsupervised high-stakes use. |
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### Recommendations |
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- Treat outputs as **reasoning simulations**. |
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- Always apply **human-in-the-loop** in sensitive contexts. |
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--- |
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## How to Get Started |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from peft import PeftModel |
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base = "meta-llama/Llama-2-7b-chat-hf" |
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adapter = "Nexus-Walker/Reson" |
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tok = AutoTokenizer.from_pretrained(base) |
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model = AutoModelForCausalLM.from_pretrained(base, device_map="auto", load_in_4bit=True) |
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model = PeftModel.from_pretrained(model, adapter) |
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prompt = "Who are you?" |
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inputs = tok(prompt, return_tensors="pt").to("cuda") |
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out = model.generate(**inputs, max_new_tokens=150) |
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print(tok.decode(out[0], skip_special_tokens=True)) |
