Instructions to use Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF 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 Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF 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 Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF: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 Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF: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 Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M
- Ollama
How to use Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF with Ollama:
ollama run hf.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF to start chatting
- Pi
How to use Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-VL-30B-A3B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF: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 Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF: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 "Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF: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"
Update README.md
Browse files
README.md
CHANGED
|
@@ -76,6 +76,52 @@ Available in Dense and MoE architectures that scale from edge to cloud, with Ins
|
|
| 76 |

|
| 77 |
|
| 78 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 79 |
|
| 80 |
## Citation
|
| 81 |
|
|
|
|
| 76 |

|
| 77 |
|
| 78 |
|
| 79 |
+
## How to Use
|
| 80 |
+
|
| 81 |
+
To use these models with `llama.cpp`, please ensure you are using the **latest version**—either by [building from source](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) or downloading the most recent [release](https://github.com/ggml-org/llama.cpp/releases/tag/b6907) according to the devices.
|
| 82 |
+
|
| 83 |
+
You can run inference via the command line or through a web-based chat interface.
|
| 84 |
+
|
| 85 |
+
### CLI Inference (`llama-mtmd-cli`)
|
| 86 |
+
|
| 87 |
+
For example, to run Qwen3-VL-2B-Instruct with an FP16 vision encoder and Q8_0 quantized LLM:
|
| 88 |
+
|
| 89 |
+
```bash
|
| 90 |
+
llama-mtmd-cli \
|
| 91 |
+
-m path/to/Qwen3VL-2B-Instruct-Q8_0.gguf \
|
| 92 |
+
--mmproj path/to/mmproj-Qwen3VL-2B-Instruct-F16.gguf \
|
| 93 |
+
--image test.jpeg \
|
| 94 |
+
-p "What is the publisher name of the newspaper?" \
|
| 95 |
+
--temp 0.7 --top-k 20 --top-p 0.8 -n 1024
|
| 96 |
+
```
|
| 97 |
+
|
| 98 |
+
### Web Chat (using `llama-server`)
|
| 99 |
+
|
| 100 |
+
To serve Qwen3-VL-235B-A22B-Instruct via an OpenAI-compatible API with a web UI:
|
| 101 |
+
|
| 102 |
+
```bash
|
| 103 |
+
llama-server \
|
| 104 |
+
-m path/to/Qwen3VL-235B-A22B-Instruct-Q4_K_M-split-00001-of-00003.gguf \
|
| 105 |
+
--mmproj path/to/mmproj-Qwen3VL-235B-A22B-Instruct-Q8_0.gguf
|
| 106 |
+
```
|
| 107 |
+
|
| 108 |
+
> **Tip**: For models split into multiple GGUF files, simply specify the first shard (e.g., `...-00001-of-00003.gguf`). llama.cpp will automatically load all parts.
|
| 109 |
+
|
| 110 |
+
Once the server is running, open your browser to `http://localhost:8080` to access the built-in chat interface, or send requests to the `/v1/chat/completions` endpoint. For more details, refer to the [official documentation](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md).
|
| 111 |
+
|
| 112 |
+
### Quantize Your Custom Model
|
| 113 |
+
|
| 114 |
+
You can further quantize the FP16 weights to other precision levels. For example, to quantize the model to 2-bit:
|
| 115 |
+
|
| 116 |
+
```bash
|
| 117 |
+
# Quantize to 2-bit (IQ2_XXS)
|
| 118 |
+
llama-quantize \
|
| 119 |
+
path/to/Qwen3VL-235B-A22B-Instruct-F16.gguf \
|
| 120 |
+
path/to/Qwen3VL-235B-A22B-Instruct-IQ2_XXS.gguf \
|
| 121 |
+
iq2_xxs 8
|
| 122 |
+
```
|
| 123 |
+
|
| 124 |
+
For a full list of supported quantization types and detailed instructions, refer to the [quantization documentation](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md).
|
| 125 |
|
| 126 |
## Citation
|
| 127 |
|