Instructions to use ElliottWise/qwen3-vl-math 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 ElliottWise/qwen3-vl-math 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 ElliottWise/qwen3-vl-math:Q4_K_M # Run inference directly in the terminal: llama cli -hf ElliottWise/qwen3-vl-math:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ElliottWise/qwen3-vl-math:Q4_K_M # Run inference directly in the terminal: llama cli -hf ElliottWise/qwen3-vl-math:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf ElliottWise/qwen3-vl-math:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ElliottWise/qwen3-vl-math:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf ElliottWise/qwen3-vl-math:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ElliottWise/qwen3-vl-math:Q4_K_M
Use Docker
docker model run hf.co/ElliottWise/qwen3-vl-math:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use ElliottWise/qwen3-vl-math with Ollama:
ollama run hf.co/ElliottWise/qwen3-vl-math:Q4_K_M
- Unsloth Desktop
- Pi
How to use ElliottWise/qwen3-vl-math with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ElliottWise/qwen3-vl-math:Q4_K_M
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": "ElliottWise/qwen3-vl-math:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ElliottWise/qwen3-vl-math with Docker Model Runner:
docker model run hf.co/ElliottWise/qwen3-vl-math:Q4_K_M
- Lemonade
How to use ElliottWise/qwen3-vl-math with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ElliottWise/qwen3-vl-math:Q4_K_M
Run and chat with the model
lemonade run user.qwen3-vl-math-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use ElliottWise/qwen3-vl-math with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ElliottWise/qwen3-vl-math:Q4_K_M
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 ElliottWise/qwen3-vl-math:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ElliottWise/qwen3-vl-math with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ElliottWise/qwen3-vl-math:Q4_K_M
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 "ElliottWise/qwen3-vl-math:Q4_K_M" \ --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 checkpoint/preprocessor_config.json from ElliottWise/qwen3-vl-math: direct link, hf CLI and curl.
- Browser
- Download file 782 Bytes
-
https://huggingface.co/ElliottWise/qwen3-vl-math/resolve/main/checkpoint/preprocessor_config.json
- Command line
-
hf download hf://ElliottWise/qwen3-vl-math/checkpoint/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/ElliottWise/qwen3-vl-math/resolve/main/checkpoint/preprocessor_config.json
782 Bytes
| { | |
| "crop_size": null, | |
| "data_format": "channels_first", | |
| "default_to_square": true, | |
| "device": null, | |
| "disable_grouping": null, | |
| "do_center_crop": null, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_pad": null, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "Qwen2VLImageProcessorFast", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "input_data_format": null, | |
| "max_pixels": null, | |
| "merge_size": 2, | |
| "min_pixels": null, | |
| "pad_size": null, | |
| "patch_size": 16, | |
| "processor_class": "Qwen3VLProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_tensors": null, | |
| "size": { | |
| "longest_edge": 16777216, | |
| "shortest_edge": 65536 | |
| }, | |
| "temporal_patch_size": 2 | |
| } | |