Instructions to use FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF",
	filename="qwen3-coder-30b-a3b-mxfp4_moe.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 FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE

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 FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE

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 FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE

Use Docker

docker model run hf.co/FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE

LM Studio
Jan

vLLM

How to use FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-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": "FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE

Ollama
How to use FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE
```

Unsloth Studio

How to use FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-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 FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-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 FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF to start chatting

How to use FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE

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": "FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-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 FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE

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 FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE
```

Lemonade

How to use FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF:MXFP4_MOE

Run and chat with the model

lemonade run user.Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF-MXFP4_MOE

List all available models

lemonade list

Qwen3-Coder-30B-A3B-Instruct-GGUF

MXFP4_MOE Quantization

This repository contains MXFP4_MOE quantized GGUF of Qwen3-Coder-30B-A3B-Instruct.

Model Details

Property	Value
Base Model	Qwen3-Coder-30B-A3B-Instruct
Architecture	Qwen3MoE
Parameters	30B (3.6B active, 128 experts, 8 activated)
Quantization	MXFP4_MOE (OCP MXFP4 E2M1, block 32, shared 8-bit block exponents)
BPW	4.47
File Size	~17.1 GB
Context Length	32K

Download

huggingface-cli download FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF qwen3-coder-30b-a3b-mxfp4_moe.gguf --local-dir . --local-dir-use-symlinks False

Quantization Information

This model uses MXFP4 (Microscaling FP4) quantization via llama.cpp's MXFP4_MOE type:

E2M1 format: 1 sign bit, 2 exponent bits, 1 mantissa bit
Block size: 32 elements sharing an 8-bit block exponent
Expert weights: Quantized to MXFP4 (3 ffn_exps tensors per layer)
Attention weights: Quantized to Q8_0 (8-bit block quantization)
Other weights: Kept in F32/F16

Verification

After download, verify the file:

echo "9f5a07e402df2aa16b9b4fcee22b5132 *qwen3-coder-30b-a3b-mxfp4_moe.gguf" | md5sum -c

Credits

Qwen Team for the base model
llama.cpp for the GGUF format and quantization tools
MXFP4 is an OCP standard microscaling FP4 format backed by AMD, NVIDIA, Microsoft, Meta, and OpenAI

Downloads last month: 264

GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

4-bit

Model tree for FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF

Base model

Qwen/Qwen3-Coder-30B-A3B-Instruct

Quantized

(145)

this model

Collection including FreedomAISVR/Qwen3-Coder-30B-A3B-MXFP4-MOE-GGUF

QWEN MXFP4 MOE GGUF

Collection

2 items • Updated 27 days ago