Instructions to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_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 ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_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 ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Qwen3-Coder-30B-A3B-Instruct-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": "ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

Ollama
How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K
```
Unsloth Desktop

How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K
```

Lemonade

How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

Run and chat with the model

lemonade run user.Qwen3-Coder-30B-A3B-Instruct-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_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 ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

need big quant like ud q8 xxl

by gopi87 - opened Aug 2, 2025

Discussion

gopi87

Aug 2, 2025

small quant are not performing well

https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF/discussions/4

ubergarm

Owner Aug 2, 2025

small quant are not performing well

I read that thread and sounds like people are having trouble is many various quants and suggest the Qwen3-30B-A3B-Thinking-2507 is doing better?

My largest IQ5_K is just a few percent higher perplexity than the baseline BF16 so it is basically as good as a Q8_0 in theory (didn't test KLD etc blah blah).

Anyway, it could just be this small coder is not as good unfortunately, maybe try the Thinking version to see if it works better for your application?

gopi87

Aug 2, 2025

small quant are not performing well

I read that thread and sounds like people are having trouble is many various quants and suggest the Qwen3-30B-A3B-Thinking-2507 is doing better?

My largest IQ5_K is just a few percent higher perplexity than the baseline BF16 so it is basically as good as a Q8_0 in theory (didn't test KLD etc blah blah).

Anyway, it could just be this small coder is not as good unfortunately, maybe try the Thinking version to see if it works better for your application?

i am just stick with q8 looks good for me tbh small model not doing very well btw kindly check this model https://huggingface.co/MetaStoneTec/XBai-o4

ubergarm

Owner Aug 2, 2025

too many new models haha... still waiting on GGUF support for some of them too so hard to quant them all until support is added. do you know if XBai-o4 has a llama.cpp PR or maybe it's architechture is already compatible?

gopi87

Aug 3, 2025

too many new models haha... still waiting on GGUF support for some of them too so hard to quant them all until support is added. do you know if XBai-o4 has a llama.cpp PR or maybe it's architechture is already compatible?

its working in llama cpp but i cant see the thinking token in the interaface but looks cool the only problem is it its a dense model yep so many moes too

gopi87

Aug 3, 2025

too many new models haha... still waiting on GGUF support for some of them too so hard to quant them all until support is added. do you know if XBai-o4 has a llama.cpp PR or maybe it's architechture is already compatible?

its working in llama cpp but i cant see the thinking token in the interaface but looks cool the only problem is it its a dense model yep so many moes too

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