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 model.json from Lutr0/OpenChoice-9B: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/Lutr0/OpenChoice-9B/resolve/main/model.json
- Command line
-
hf download hf://Lutr0/OpenChoice-9B/model.json
-
curl -L -o model.json https://huggingface.co/Lutr0/OpenChoice-9B/resolve/main/model.json
1.09 kB
| { | |
| "config": { | |
| "model_id": "Qwen/Qwen3.5-9B", | |
| "revision": "c202236235762e1c871ad0ccb60c8ee5ba337b9a", | |
| "initialization": "pretrained", | |
| "dtype": "bfloat16", | |
| "arm": "slot", | |
| "head_width": 128, | |
| "lora_rank": 16, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0, | |
| "slot_loss": "candidate", | |
| "max_sequence_tokens": 4096, | |
| "max_decision_tokens": 32768, | |
| "gradient_checkpointing": true, | |
| "seed": 20260921 | |
| }, | |
| "phase": "joint", | |
| "processor_contract": { | |
| "model_id": "Qwen/Qwen3.5-9B", | |
| "revision": "c202236235762e1c871ad0ccb60c8ee5ba337b9a", | |
| "tokenizer_hash": "467543819daf0b32f85b6b8e9097aa1ac1675affa59836c26acbc4a22eb83f0f" | |
| }, | |
| "files": { | |
| "adapter/README.md": "4e6784385d9fb97d6442c6338f603c62d35d9d94406152ff96aad8385c354426", | |
| "adapter/adapter_config.json": "785be308d2b81bda7b66880a05b8a45b203510e23a6383bfb7531cd2fbf5b326", | |
| "adapter/adapter_model.safetensors": "1d44ed76145d99ad8ee50f3b3c20a7aca965a6890f8a45fbf8fbf683ca23ec80", | |
| "heads.safetensors": "9bbcbf73561f6bc5d0a17ea6a2081feed2d1304e87602d8c502d9a5c4bd85576" | |
| } | |
| } | |