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

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 Settings

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

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

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

Scored 65.00 on MMLU Pro single shot 🔥

by xbruce22 - opened Aug 4, 2025

Discussion

xbruce22

Aug 4, 2025

logs

+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| Model                                        | Dataset   | Metric          | Subset           |   Num |   Score | Cat.0   |
+==============================================+===========+=================+==================+=======+=========+=========+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | computer science |    10 |    0.6  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | math             |    10 |    0.8  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | chemistry        |    10 |    0.7  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | engineering      |    10 |    0.7  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | law              |    10 |    0.3  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | biology          |    10 |    0.9  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | health           |    10 |    0.8  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | physics          |    10 |    0.6  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | business         |    10 |    0.6  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | philosophy       |    10 |    0.6  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | economics        |    10 |    0.8  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | other            |    10 |    0.6  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | psychology       |    10 |    0.6  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | history          |    10 |    0.5  | default |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+
| intel-Qwen3-Coder-30B-A3B-Instruct-gguf-q2ks | mmlu_pro  | AverageAccuracy | OVERALL          |   140 |    0.65 | -       |
+----------------------------------------------+-----------+-----------------+------------------+-------+---------+---------+

PussyHut

Aug 4, 2025

•

edited Aug 4, 2025

for comparison:

unsloth/Qwen3-Coder-30B-A3B-Instruct-1M-GGUF
Score | Model | GGUF Size
63.57 Q4_K_M 18.6GB (unsloth)
65.71 Q4_K_M ~19GB (ollama)

from MMLU Pro benchmark

Haihao

Intel org Aug 4, 2025

•

edited Aug 5, 2025

for comparison:

unsloth/Qwen3-Coder-30B-A3B-Instruct-1M-GGUF
Score | Model | GGUF Size
63.57 Q4_K_M 18.6GB (unsloth)
65.71 Q4_K_M ~19GB (ollama)

from MMLU Pro benchmark

Are the above two scores also coming from AVERAGE?

xbruce22

Aug 5, 2025

Yes OVERALL result (as you can see in above logs) across all subjects.

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