Instructions to use Nexus-Walker/Reson with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nexus-Walker/Reson", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
Commit History
28 days ago
-
Clarify Reson licensing and model documentation
Verified signatureaca599b
-
Update README.md
Verified signature41ac4d3
-
Update README.md
Verified signature93be4da
-
Update README.md
Verified signature## Extended Description ⚠️ Important: Reson does **not hallucinate** in the usual sense — it is trained to **adapt**. Outputs may look unconventional or speculative because the model’s objective is **meta-cognition and adaptive strategy**, not factual recall. Reson is a LoRA fine-tune of LLaMA-2 7B Chat trained on ~11k custom instruction/response pairs. It explores: - How a model can **reflect on its own reasoning**, - How it adapts to **multi-domain scenarios** (business, tactical, scientific), - How far **strategic simulation** can go with lightweight fine-tuning. Resources: - [Chat demo script](./chat.py) - [Example transcripts](./demo_chat.md) 🚀 Goal: provide a testbed for **cognitive AI behaviors** that go beyond factual QA, enabling adaptation under uncertainty.39f2da9 -
Add Reson 4.5 model files
Daniele Cangi9db84cb
-
initial commit
Verified signaturea4e30e1