Instructions to use eaddario/Qwen3-30B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use eaddario/Qwen3-30B-A3B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="eaddario/Qwen3-30B-A3B-GGUF", filename="Qwen3-30B-A3B-F16-00001-of-00003.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use eaddario/Qwen3-30B-A3B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf eaddario/Qwen3-30B-A3B-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 eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf eaddario/Qwen3-30B-A3B-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 eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use eaddario/Qwen3-30B-A3B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "eaddario/Qwen3-30B-A3B-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": "eaddario/Qwen3-30B-A3B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
- Ollama
How to use eaddario/Qwen3-30B-A3B-GGUF with Ollama:
ollama run hf.co/eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
- Unsloth Studio
How to use eaddario/Qwen3-30B-A3B-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 eaddario/Qwen3-30B-A3B-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 eaddario/Qwen3-30B-A3B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for eaddario/Qwen3-30B-A3B-GGUF to start chatting
- Pi
How to use eaddario/Qwen3-30B-A3B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf eaddario/Qwen3-30B-A3B-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": "eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use eaddario/Qwen3-30B-A3B-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 eaddario/Qwen3-30B-A3B-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 eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use eaddario/Qwen3-30B-A3B-GGUF with Docker Model Runner:
docker model run hf.co/eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
- Lemonade
How to use eaddario/Qwen3-30B-A3B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-30B-A3B-GGUF-Q4_K_M
List all available models
lemonade list
| ====== Perplexity statistics ====== | |
| Mean PPL(Q) : 9.141469 ± 0.071687 | |
| Mean PPL(base) : 8.445938 ± 0.065177 | |
| Cor(ln(PPL(Q)), ln(PPL(base))): 97.40% | |
| Mean ln(PPL(Q)/PPL(base)) : 0.079135 ± 0.001778 | |
| Mean PPL(Q)/PPL(base) : 1.082351 ± 0.001925 | |
| Mean PPL(Q)-PPL(base) : 0.695531 ± 0.016891 | |
| ====== KL divergence statistics ====== | |
| Mean KLD: 0.115653 ± 0.000636 | |
| Maximum KLD: 16.755222 | |
| 99.9% KLD: 2.903406 | |
| 99.0% KLD: 1.030481 | |
| 99.0% KLD: 1.030481 | |
| Median KLD: 0.051309 | |
| 10.0% KLD: 0.000107 | |
| 5.0% KLD: 0.000012 | |
| 1.0% KLD: 0.000000 | |
| Minimum KLD: -0.000004 | |
| ====== Token probability statistics ====== | |
| Mean Δp: -1.357 ± 0.027 % | |
| Maximum Δp: 99.787% | |
| 99.9% Δp: 54.627% | |
| 99.0% Δp: 26.888% | |
| 95.0% Δp: 11.789% | |
| 90.0% Δp: 5.843% | |
| 75.0% Δp: 0.415% | |
| Median Δp: -0.011% | |
| 25.0% Δp: -2.096% | |
| 10.0% Δp: -10.461% | |
| 5.0% Δp: -18.305% | |
| 1.0% Δp: -39.181% | |
| 0.1% Δp: -79.693% | |
| Minimum Δp: -99.947% | |
| RMS Δp : 10.469 ± 0.057 % | |
| Same top p: 85.489 ± 0.091 % | |