Instructions to use Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound", filename="Qwen3-Coder-30B-A3B-Instruct-128x1.8B-Q2_K_S.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S # Run inference directly in the terminal: llama-cli -hf Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S # Run inference directly in the terminal: llama-cli -hf Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S
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 Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S # Run inference directly in the terminal: ./llama-cli -hf Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S
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 Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S
Use Docker
docker model run hf.co/Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S
- LM Studio
- Jan
- Ollama
How to use Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound with Ollama:
ollama run hf.co/Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S
- Unsloth Studio new
How to use Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound 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 Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound 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 Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound to start chatting
- Pi new
How to use Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S
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": "Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S
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 Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S
Run Hermes
hermes
- Docker Model Runner
How to use Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound with Docker Model Runner:
docker model run hf.co/Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S
- Lemonade
How to use Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Intel/Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound:Q2_K_S
Run and chat with the model
lemonade run user.Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks-mixed-AutoRound-Q2_K_S
List all available models
lemonade list
Can't wait to see the benchmark
Yes, in queue
Scored 65.00. Added logs in discussion.
Scored 65.00. Added logs in discussion.
better than unsloth's Q4? That's shocking.
Yes, I don't know why.
Let me check Q2 of unsloth.
Scored 0.6 Q2_K
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Model | Dataset | Metric | Subset | Num | Score | Cat.0 |
+===========================================+===========+=================+==================+=======+=========+=========+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | computer science | 10 | 0.6 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | math | 10 | 0.6 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | chemistry | 10 | 0.8 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | engineering | 10 | 0.5 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | law | 10 | 0.1 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | biology | 10 | 0.9 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | health | 10 | 0.8 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | physics | 10 | 0.6 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | business | 10 | 0.7 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | philosophy | 10 | 0.5 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | economics | 10 | 0.7 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | other | 10 | 0.7 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | psychology | 10 | 0.4 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | history | 10 | 0.5 | default |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Qwen3-Coder-30B-A3B-Instruct-1M-GGUF-Q2_K | mmlu_pro | AverageAccuracy | OVERALL | 140 | 0.6 | - |
+-------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
Scored 0.6 Q2_K
damn, seems the capability shrink from Q4 to Q2 for LLMs was lower than I had expected before
0.6 means 60.00 BTW.
Thanks @xbruce22 @Push . Based on the logs and the above discussion, does it mean that Intel Q2 model (0.65) achieves higher accuracy than unsloth Q2 model (0.6)?
Yes, So far I have tested 2 models from Intel, Qwen 30B coder and instruct. Even instruct works better with Intel's quantized version, check our here
71.2 Q2_K_S.gguf 10.7GB (from intel)
70.7 Q2_K.gguf 11.3GB (from unsloth)
76.0 Q8_0.gguf 32.5GB (from unsloth)
Let me check Q2 of unsloth.
& intel q4 k m π
Scored 64.29
thanks for your time
Intel q2ks even beat their own q4km π§ am I dreaming?
considering that each sub-item was run 10 times, can we say that there is no significant deviation?
Check the size and some ops are in higher precision in Q2 model which means it's more sensitive to the accuracy.