Instructions to use Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF",
	filename="Qwen3-Coder-REAP-25B-A3B-Rust-IQ1_M.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 Em-80/Qwen3-coder-REAP-25B-A3B-Rust-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 Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Em-80/Qwen3-coder-REAP-25B-A3B-Rust-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 Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Em-80/Qwen3-coder-REAP-25B-A3B-Rust-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 Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Em-80/Qwen3-coder-REAP-25B-A3B-Rust-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": "Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M

Ollama
How to use Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF with Ollama:
```
ollama run hf.co/Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M
```

Unsloth Studio

How to use Em-80/Qwen3-coder-REAP-25B-A3B-Rust-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 Em-80/Qwen3-coder-REAP-25B-A3B-Rust-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 Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF to start chatting

How to use Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Em-80/Qwen3-coder-REAP-25B-A3B-Rust-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": "Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Em-80/Qwen3-coder-REAP-25B-A3B-Rust-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 Em-80/Qwen3-coder-REAP-25B-A3B-Rust-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 Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M

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 "Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M" \
  --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"

Docker Model Runner
How to use Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF with Docker Model Runner:
```
docker model run hf.co/Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M
```

Lemonade

How to use Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-coder-REAP-25B-A3B-Rust-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3-coder-REAP-25B-A3B-Rust-GGUF

This repository provides high-precision quantized versions of the Qwen3-Coder-REAP-25B-A3B model, featuring a custom importance matrix generated by Em-80, and specifically optimized for Rust code generation.

Note

I included the .imatrix if you need to quant your own version. Just please link to me if you use the imatrix and publish the model.

All the quants I'm planning to do are up.
Benchmarks do to time contraints in my personal life I have no idea if I will ever get to these, if any of you happen to run bunchmarks please open a discussion and share them, I will then add them to the model card with credit when I have time, Thank you very much if you do share benchmarks.

Highlights

Model Architecture: Based on the Cerebras REAP (Router-weighted Expert Activation Pruning) variant of Qwen3-Coder-30B-A3B-Instruct.
Custom Imatrix: Includes Qwen3-Coder-REAP-25B-A3B-Rust.imatrix, generated using a diverse and high-density calibration set.
Optimized for Logic: The quantization process focused heavily on maintaining the model's multi-lingual coding and mathematical reasoning capabilities.

Importance Matrix (Imatrix) Details

The included .imatrix file was developed to ensure that lower-bit quants retain as much intelligence as possible. Unlike standard "blind" quants that use generic calibration data, this imatrix was derived from a curated 3,000-sample dataset(21.8 MB) covering:

Programming: Deep coverage of Rust and Python syntax, logic, and idiomatic patterns.
Reasoning: Advanced Mathematics and logical proofs.
Linguistic Quality: High-quality English prose.

By using this importance matrix during the quantization process, we ensure that the weights most critical for code generation and complex problem-solving are preserved with higher fidelity.

Files Included

Weights: Multiple quantization levels (GGUF).
Metadata: Qwen3-Coder-REAP-25B-A3B-Rust.imatrix for users who wish to perform their own custom quantization runs.

Licensing and Attribution

This work is a derivative of the Qwen3-Coder-REAP-25B-A3B model by Cerebras Systems and Qwen3-Coder-30B-A3B-Instruct by Alibaba Cloud.

Weights & Imatrix: Released under the Apache License 2.0.
Attribution: Modifications, quantization, and imatrix generation performed by Em-80.

Please refer to the LICENSE file in this repository for full legal terms and modification notices.

Usage

To use these quants with or quant your own Qwen3-Coder-REAP-25B-A3B with the provided imatrix in llama.cpp:

Example for running a quant

./main -m Qwen3-Coder-REAP-25B-A3B-Rust-IQ3_M.gguf -p "Write a thread-safe singleton in Rust."

Notice: This model is provided "as-is" without warranty of any kind. Use at your own risk.

Downloads last month: 3,553

GGUF

Model size

25B params

Architecture

qwen3moe

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

Model tree for Em-80/Qwen3-coder-REAP-25B-A3B-Rust-GGUF

Base model

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

Finetuned

cerebras/Qwen3-Coder-REAP-25B-A3B

Quantized

(28)

this model