Instructions to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bartowski/Qwen2.5-Coder-32B-Instruct-GGUF",
	filename="Qwen2.5-Coder-32B-Instruct-IQ2_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/Qwen2.5-Coder-32B-Instruct-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 bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/Qwen2.5-Coder-32B-Instruct-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 bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/Qwen2.5-Coder-32B-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": "bartowski/Qwen2.5-Coder-32B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

Ollama
How to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with Ollama:
```
ollama run hf.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use bartowski/Qwen2.5-Coder-32B-Instruct-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 bartowski/Qwen2.5-Coder-32B-Instruct-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 bartowski/Qwen2.5-Coder-32B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bartowski/Qwen2.5-Coder-32B-Instruct-GGUF to start chatting

Pi new

How to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf bartowski/Qwen2.5-Coder-32B-Instruct-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": "bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bartowski/Qwen2.5-Coder-32B-Instruct-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 bartowski/Qwen2.5-Coder-32B-Instruct-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 bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Coder-32B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Difference between this repo and the official GGUF repo?

by mhenning - opened Feb 26, 2025

Discussion

mhenning

Feb 26, 2025

•

edited Feb 26, 2025

I'm new to all of this quantization, so excuse my question if it's obvious. What is the difference between this repo's GGUF models and e.g. the official Qwen GGUF model? (https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct-GGUF) Or from e.g. unsloth?
I found "All quants made using imatrix option with dataset from here(link)" but I can't figure out what that means or if it contributes to any differences. Or is it "just" to have a(nother) repo with quants? Which would be a valid point in itself.
Edit: I see that your repo also has more quantization types. My question is more on the differences between the same quantization, for example Q4_K_M, which both repos have.

PS: Thank you for providing all these models!

bartowski

Owner Feb 26, 2025

Imatrix is an attempt to improve overall quality of the model while using the same bits per weight and structure by using a corpus of data (the one I linked) to count the activations of each weight in the model, thus finding the "important" weights. This information is then used to make a more informed decision when selecting the rounding values like scale and offset, so that the important weights are more accurately represented in the final result

You can find a bit more info from the original Reddit discussion here:

https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/?rdt=35657

Jokest

Feb 27, 2025

Thanks for your answer! As a follow-up question, I noticed that in the official repo, the models are quantized using AWQ (Activation-aware Weight Quantization), are you also using that (on top of imatrix)? I tried to Google around but didn't find an appropriate answer.

bartowski

Owner Feb 27, 2025

No I'm not using that as well, there used to be an implementation in llama.cpp to apply AWQ before conversion but I think it got removed at some point?

https://github.com/ggml-org/llama.cpp/pull/5768

It's possible Qwen maintained their own branch since I know they also have released some GGUFs that don't line up with the official GGUF implementation

rrrav

Feb 27, 2025

I ve , been trying your uploads for more than a year, they are awesome, I always wanted to thank you but sorry for been so late , finally Thanks from my sincere heart. and 1 more ? . where do u get this energy from. don't you unplug. if u , next time, visit arambol. you may have known about arambol,goa,india. if not, sure worth. a try. I may then get a in person to thank u. so ..
thanks again.ravi

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