Instructions to use Habibur2/Qwen2.5-0.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Habibur2/Qwen2.5-0.5B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Habibur2/Qwen2.5-0.5B-GGUF",
	filename="qwen-2.5-0.5b-q4_0.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 Habibur2/Qwen2.5-0.5B-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 Habibur2/Qwen2.5-0.5B-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf Habibur2/Qwen2.5-0.5B-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Habibur2/Qwen2.5-0.5B-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf Habibur2/Qwen2.5-0.5B-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 Habibur2/Qwen2.5-0.5B-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf Habibur2/Qwen2.5-0.5B-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 Habibur2/Qwen2.5-0.5B-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Habibur2/Qwen2.5-0.5B-GGUF:Q4_0

Use Docker

docker model run hf.co/Habibur2/Qwen2.5-0.5B-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use Habibur2/Qwen2.5-0.5B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Habibur2/Qwen2.5-0.5B-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": "Habibur2/Qwen2.5-0.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Habibur2/Qwen2.5-0.5B-GGUF:Q4_0

Ollama
How to use Habibur2/Qwen2.5-0.5B-GGUF with Ollama:
```
ollama run hf.co/Habibur2/Qwen2.5-0.5B-GGUF:Q4_0
```
Unsloth Desktop

How to use Habibur2/Qwen2.5-0.5B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Habibur2/Qwen2.5-0.5B-GGUF:Q4_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Habibur2/Qwen2.5-0.5B-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Habibur2/Qwen2.5-0.5B-GGUF with Docker Model Runner:
```
docker model run hf.co/Habibur2/Qwen2.5-0.5B-GGUF:Q4_0
```

Lemonade

How to use Habibur2/Qwen2.5-0.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Habibur2/Qwen2.5-0.5B-GGUF:Q4_0

Run and chat with the model

lemonade run user.Qwen2.5-0.5B-GGUF-Q4_0

List all available models

lemonade list

Hermes Agent

How to use Habibur2/Qwen2.5-0.5B-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 Habibur2/Qwen2.5-0.5B-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 Habibur2/Qwen2.5-0.5B-GGUF:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Habibur2/Qwen2.5-0.5B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Habibur2/Qwen2.5-0.5B-GGUF:Q4_0

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 "Habibur2/Qwen2.5-0.5B-GGUF:Q4_0" \
  --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"

Qwen 2.5 0.5B Instruct (GGUF Quantized)

This repository contains the GGUF quantized version of the Qwen 2.5 0.5B Instruct model. It is an Ultra-Lightweight Micro SLM designed for edge devices, mobile phones, and IoT applications.

Model Creator: Qwen Team (Alibaba Cloud)
Quantized By: Md Habibur Rahman (Aasif)
Quantization Format: GGUF (Q4_0)
Target Device: Android, Raspberry Pi, Low-end Laptops

⚡ Performance

This model is extremely fast and requires minimal RAM.

Metric	Value
Model Size	~350 MB
RAM Required	< 1 GB
Parameters	0.5 Billion
Speed (GPU)	100+ Tokens/sec (Est.)

🚀 Usage Code

from huggingface_hub import hf_hub_download
from llama_cpp import Llama

model_path = hf_hub_download(
    repo_id="Habibur2/Qwen2.5-0.5B-GGUF",
    filename="qwen-2.5-0.5b-q4_0.gguf"
)

llm = Llama(model_path=model_path, n_ctx=1024, n_gpu_layers=-1)

response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Write a hello world code in Python."}]
)

print(response['choices'][0]['message']['content'])

Downloads last month: 29

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Habibur2/Qwen2.5-0.5B-GGUF

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

(327)

this model