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

Libraries

How to use lmstudio-community/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="lmstudio-community/Qwen2.5-Coder-32B-Instruct-GGUF",
	filename="Qwen2.5-Coder-32B-Instruct-Q3_K_L.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 lmstudio-community/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 lmstudio-community/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lmstudio-community/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 lmstudio-community/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lmstudio-community/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 lmstudio-community/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lmstudio-community/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 lmstudio-community/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lmstudio-community/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use lmstudio-community/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 "lmstudio-community/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": "lmstudio-community/Qwen2.5-Coder-32B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio new

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

Pi new

How to use lmstudio-community/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 lmstudio-community/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": "lmstudio-community/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lmstudio-community/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

💫 Community Model> Qwen2.5 Coder 32B Instruct by Qwen

👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord.

Model creator: Qwen
Original model: Qwen2.5-Coder-32B-Instruct
GGUF quantization: provided by bartowski based on llama.cpp release b4014

Technical Details

Long-context Support up to 128K tokens with yarn rope scaling factor of 4.0

Designed for use with Code Agents

Up to 5.5 trillion training tokens including source code, text-code grounding, and synthetic data

Special thanks

🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

Disclaimers

LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.

Downloads last month: 2,031

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

3-bit

4-bit

6-bit

8-bit

Model tree for lmstudio-community/Qwen2.5-Coder-32B-Instruct-GGUF

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-Coder-32B

Finetuned

Qwen/Qwen2.5-Coder-32B-Instruct

Quantized

(120)

this model