Instructions to use ArtusDev/Qwen3-235B-A22B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ArtusDev/Qwen3-235B-A22B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ArtusDev/Qwen3-235B-A22B-GGUF", filename="Qwen3-235B-A22B-mix-IQ6_K-00001-of-00005.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use ArtusDev/Qwen3-235B-A22B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K # Run inference directly in the terminal: llama-cli -hf ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K # Run inference directly in the terminal: llama-cli -hf ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
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 ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K # Run inference directly in the terminal: ./llama-cli -hf ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
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 ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
Use Docker
docker model run hf.co/ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
- LM Studio
- Jan
- vLLM
How to use ArtusDev/Qwen3-235B-A22B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ArtusDev/Qwen3-235B-A22B-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": "ArtusDev/Qwen3-235B-A22B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
- Ollama
How to use ArtusDev/Qwen3-235B-A22B-GGUF with Ollama:
ollama run hf.co/ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
- Unsloth Studio new
How to use ArtusDev/Qwen3-235B-A22B-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 ArtusDev/Qwen3-235B-A22B-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 ArtusDev/Qwen3-235B-A22B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ArtusDev/Qwen3-235B-A22B-GGUF to start chatting
- Pi new
How to use ArtusDev/Qwen3-235B-A22B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
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": "ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ArtusDev/Qwen3-235B-A22B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
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 ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
Run Hermes
hermes
- Docker Model Runner
How to use ArtusDev/Qwen3-235B-A22B-GGUF with Docker Model Runner:
docker model run hf.co/ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
- Lemonade
How to use ArtusDev/Qwen3-235B-A22B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ArtusDev/Qwen3-235B-A22B-GGUF:Q6_K
Run and chat with the model
lemonade run user.Qwen3-235B-A22B-GGUF-Q6_K
List all available models
lemonade list
ik_llama.cpp imatrix Quantizations of Qwen/Qwen3-235B-A22B
This quant collection REQUIRES ik_llama.cpp fork to support advanced non-linear SotA quants. Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!
These quants provide best in class quality for the given memory footprint.
Big Thanks
Shout out to @ubergarm for his diligent work on ik_llama.cpp oriented quanting.
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Model tree for ArtusDev/Qwen3-235B-A22B-GGUF
Base model
Qwen/Qwen3-235B-A22B