Instructions to use Brianpuz/Qwen2-0.5B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Brianpuz/Qwen2-0.5B-Instruct-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 Brianpuz/Qwen2-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Brianpuz/Qwen2-0.5B-Instruct-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 Brianpuz/Qwen2-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Brianpuz/Qwen2-0.5B-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 Brianpuz/Qwen2-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Brianpuz/Qwen2-0.5B-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 Brianpuz/Qwen2-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Brianpuz/Qwen2-0.5B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Brianpuz/Qwen2-0.5B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Brianpuz/Qwen2-0.5B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Brianpuz/Qwen2-0.5B-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": "Brianpuz/Qwen2-0.5B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Brianpuz/Qwen2-0.5B-Instruct-GGUF:Q4_K_M

Ollama
How to use Brianpuz/Qwen2-0.5B-Instruct-GGUF with Ollama:
```
ollama run hf.co/Brianpuz/Qwen2-0.5B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Brianpuz/Qwen2-0.5B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Brianpuz/Qwen2-0.5B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use Brianpuz/Qwen2-0.5B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Brianpuz/Qwen2-0.5B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2-0.5B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Produced by Antigma Labs

Follow Antigma Labs in X https://x.com/antigma_labs

Antigma's GitHub Homepage https://github.com/AntigmaLabs

llama.cpp quantization

Using llama.cpp release b4944 for quantization. Original model: https://huggingface.co/Qwen/Qwen2-0.5B-Instruct Run them directly with llama.cpp, or any other llama.cpp based project

Prompt format

<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><｜end▁of▁sentence｜><｜Assistant｜>

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Split
qwen2-0.5b-instruct-q4_k_m.gguf	Q4_K_M	0.37 GB	False

Downloading using huggingface-cli

Click to view download instructions

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download https://huggingface.co/Brianpuz/Qwen2-0.5B-Instruct-GGUF --include "qwen2-0.5b-instruct-q4_k_m.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download https://huggingface.co/Brianpuz/Qwen2-0.5B-Instruct-GGUF --include "qwen2-0.5b-instruct-q4_k_m.gguf/*" --local-dir ./

You can either specify a new local-dir (deepseek-ai_DeepSeek-V3-0324-Q8_0) or download them all in place (./)

Downloads last month: 16

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Brianpuz/Qwen2-0.5B-Instruct-GGUF

Base model

Qwen/Qwen2-0.5B

Finetuned

Qwen/Qwen2-0.5B-Instruct

Quantized

(93)

this model