Zero-Shot Classification
Safetensors
GGUF
English
openchoice
decision-model
lora
qwen3.5
cpu
vision
typesafe-compatible
conversational
Instructions to use Lutr0/OpenChoice-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Lutr0/OpenChoice-9B with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Lutr0/OpenChoice-9B:Q8_0 # Run inference directly in the terminal: llama cli -hf Lutr0/OpenChoice-9B:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Lutr0/OpenChoice-9B:Q8_0 # Run inference directly in the terminal: llama cli -hf Lutr0/OpenChoice-9B:Q8_0
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Lutr0/OpenChoice-9B:Q8_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf Lutr0/OpenChoice-9B:Q8_0
Use Docker
docker model run hf.co/Lutr0/OpenChoice-9B:Q8_0
- Llama new
- LM Studio
- Jan
- Ollama
How to use Lutr0/OpenChoice-9B with Ollama:
ollama run hf.co/Lutr0/OpenChoice-9B:Q8_0
- Unsloth Desktop
- Pi
How to use Lutr0/OpenChoice-9B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Lutr0/OpenChoice-9B:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Lutr0/OpenChoice-9B:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Lutr0/OpenChoice-9B with Docker Model Runner:
docker model run hf.co/Lutr0/OpenChoice-9B:Q8_0
- Lemonade
How to use Lutr0/OpenChoice-9B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lutr0/OpenChoice-9B:Q8_0
Run and chat with the model
lemonade run user.OpenChoice-9B-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use Lutr0/OpenChoice-9B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Lutr0/OpenChoice-9B:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Lutr0/OpenChoice-9B:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Lutr0/OpenChoice-9B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Lutr0/OpenChoice-9B:Q8_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Lutr0/OpenChoice-9B:Q8_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download NOTICE from Lutr0/OpenChoice-9B: direct link, hf CLI and curl.
- Browser
- Download file 1.52 kB
-
https://huggingface.co/Lutr0/OpenChoice-9B/resolve/main/NOTICE
- Command line
-
hf download hf://Lutr0/OpenChoice-9B/NOTICE
-
curl -L -o NOTICE https://huggingface.co/Lutr0/OpenChoice-9B/resolve/main/NOTICE
1.52 kB
| OpenChoice-9B NOTICE | |
| Base model: Qwen/Qwen3.5-9B, © Alibaba Cloud, Apache-2.0. This package includes derived merged/quantized text weights and a quantized base vision projector. Upstream base terms remain applicable; see LICENSE-QWEN. The original LoRA adapter remains separately available in adapter/. | |
| Training-source attributions and maintainer-recorded license evidence: | |
| - NVIDIA Aegis AI Content Safety Dataset 2.0, CC-BY-4.0 (human prompt labels only): https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0 | |
| - BoolQ, CC-BY-SA-3.0: https://huggingface.co/datasets/google/boolq | |
| - NVIDIA HelpSteer2, CC-BY-4.0 (human attribute ratings; model-generated responses): https://huggingface.co/datasets/nvidia/HelpSteer2 | |
| - Stanford SNLI, CC-BY-SA-4.0: https://huggingface.co/datasets/stanfordnlp/snli | |
| - 400 owned OpenChoice replay rows: internally authored material; no public license asserted. | |
| Evaluation only: SciQ, Johannes Welbl, Nelson F. Liu and Matt Gardner, "Crowdsourcing Multiple Choice Science Questions" (2017), https://arxiv.org/abs/1707.06209; https://huggingface.co/datasets/allenai/sciq (CC-BY-NC-3.0 according to its card). SciQ was not used in project fine-tuning, and its input text is not redistributed here. | |
| This notice is attribution, not a grant or legal advice about whether and how the source licenses apply to the weights. Code is Apache-2.0 in the separate GitHub repository (https://github.com/lutr0/openchoice); the model package declares its own CC-BY-SA-4.0 choice. |