Instructions to use mukel/Qwen2.5-3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mukel/Qwen2.5-3B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mukel/Qwen2.5-3B-Instruct-GGUF",
	filename="Qwen2.5-3B-Instruct-Q4_0.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 mukel/Qwen2.5-3B-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 mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0

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 mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0

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 mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0

Use Docker

docker model run hf.co/mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use mukel/Qwen2.5-3B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mukel/Qwen2.5-3B-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": "mukel/Qwen2.5-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0

Ollama
How to use mukel/Qwen2.5-3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0
```

Unsloth Studio new

How to use mukel/Qwen2.5-3B-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 mukel/Qwen2.5-3B-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 mukel/Qwen2.5-3B-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 mukel/Qwen2.5-3B-Instruct-GGUF to start chatting

Pi new

How to use mukel/Qwen2.5-3B-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 mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0

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": "mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mukel/Qwen2.5-3B-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 mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0

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 mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0

Run Hermes

hermes

Docker Model Runner
How to use mukel/Qwen2.5-3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0
```

Lemonade

How to use mukel/Qwen2.5-3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mukel/Qwen2.5-3B-Instruct-GGUF:Q4_0

Run and chat with the model

lemonade run user.Qwen2.5-3B-Instruct-GGUF-Q4_0

List all available models

lemonade list

GGUF models for qwen2.java

Pure .gguf Q4_0 and Q8_0 quantizations of Qwen 2.5 models, ready to consume by qwen2.java.

In the wild, Q8_0 quantizations are fine, but Q4_0 quantizations are rarely pure e.g. the token embeddings are quantized with Q6_K, instead of Q4_0.
A pure Q4_0 quantization can be generated from a high precision (F32, F16, BFLOAT16) .gguf source with the llama-quantize utility from llama.cpp as follows:

./llama-quantize --pure ./Qwen-2.5-7B-Instruct-BF16.gguf ./Qwen-2.5-7B-Instruct-Q4_0.gguf Q4_0

Introduction

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:

Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
Long-context Support up to 128K tokens and can generate up to 8K tokens.
Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.