Instructions to use Nexus-Walker/Reson with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nexus-Walker/Reson with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
model = PeftModel.from_pretrained(base_model, "Nexus-Walker/Reson")

Transformers

How to use Nexus-Walker/Reson with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Nexus-Walker/Reson")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Nexus-Walker/Reson", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Nexus-Walker/Reson with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nexus-Walker/Reson"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nexus-Walker/Reson",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nexus-Walker/Reson

SGLang

How to use Nexus-Walker/Reson with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Nexus-Walker/Reson" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nexus-Walker/Reson",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Nexus-Walker/Reson" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nexus-Walker/Reson",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nexus-Walker/Reson with Docker Model Runner:
```
docker model run hf.co/Nexus-Walker/Reson
```

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
⚠️CLI chat " I highly recommend using the chat file because it is optimized and balanced for the Reson model": 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

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))

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Article mentioning Nexus-Walker/Reson

Reson: Teaching AI to think about Its own thinking

Nexus-Walker

•

Sep 12, 2025

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