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
File size: 1,068 Bytes
6db6db3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | ====== Perplexity statistics ======
Mean PPL(Q) : 8.674113 ± 0.067361
Mean PPL(base) : 8.445938 ± 0.065177
Cor(ln(PPL(Q)), ln(PPL(base))): 98.92%
Mean ln(PPL(Q)/PPL(base)) : 0.026657 ± 0.001137
Mean PPL(Q)/PPL(base) : 1.027016 ± 0.001168
Mean PPL(Q)-PPL(base) : 0.228175 ± 0.009969
====== KL divergence statistics ======
Mean KLD: 0.043268 ± 0.000351
Maximum KLD: 18.135883
99.9% KLD: 1.491586
99.0% KLD: 0.417118
99.0% KLD: 0.417118
Median KLD: 0.016406
10.0% KLD: 0.000034
5.0% KLD: 0.000004
1.0% KLD: -0.000000
Minimum KLD: -0.000026
====== Token probability statistics ======
Mean Δp: -0.672 ± 0.017 %
Maximum Δp: 98.835%
99.9% Δp: 40.761%
99.0% Δp: 17.002%
95.0% Δp: 6.983%
90.0% Δp: 3.441%
75.0% Δp: 0.273%
Median Δp: -0.003%
25.0% Δp: -1.108%
10.0% Δp: -5.679%
5.0% Δp: -10.036%
1.0% Δp: -23.278%
0.1% Δp: -54.994%
Minimum Δp: -99.427%
RMS Δp : 6.596 ± 0.049 %
Same top p: 91.212 ± 0.073 %
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