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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF",
	filename="Qwen2.5-0.5B-Instruct-F16.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 ThomasBaruzier/Qwen2.5-0.5B-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 ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ThomasBaruzier/Qwen2.5-0.5B-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 ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ThomasBaruzier/Qwen2.5-0.5B-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 ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M

Ollama
How to use ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF with Ollama:
```
ollama run hf.co/ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use ThomasBaruzier/Qwen2.5-0.5B-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 ThomasBaruzier/Qwen2.5-0.5B-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 ThomasBaruzier/Qwen2.5-0.5B-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 ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF to start chatting

How to use ThomasBaruzier/Qwen2.5-0.5B-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 ThomasBaruzier/Qwen2.5-0.5B-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": "ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ThomasBaruzier/Qwen2.5-0.5B-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 ThomasBaruzier/Qwen2.5-0.5B-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 ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ThomasBaruzier/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-0.5B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

ThomasBaruzier commited on Sep 19, 2024

Commit

43933b7

verified ·

1 Parent(s): d3b9250

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+Qwen2.5-0.5B-Instruct
+Quant	Size (MB)	PPL	Size (%)	Accuracy (%)	PPL error rate
+IQ1_S	302	30.0453			0.23714
+IQ1_M	304	24.9151			0.19238
+IQ2_XXS	307	21.4864			0.16704
+IQ2_XS	310	19.7829			0.15355
+IQ2_S	311	19.2041			0.14841
+IQ2_M	314	18.3250			0.14001
+Q2_K_S	316	18.3462			0.14091
+IQ3_XXS	319	16.9784			0.12828
+Q3_K_S	323	17.7765			0.13668
+IQ3_S	323	16.3794			0.12173
+IQ3_XS	323	16.3794			0.12173
+Q2_K	323	17.6841			0.13561
+IQ3_M	327	16.3667			0.12182
+IQ4_XS	334	15.8792			0.11933
+IQ4_NL	337	15.8468			0.11921
+Q4_0	337	17.1007			0.13053
+Q3_K_M	339	15.8499			0.11934
+Q3_K_L	353	15.7298			0.11820
+Q4_1	358	16.1819			0.12328
+Q4_K_S	368	15.5497			0.11716
+Q5_0	380	15.5038			0.11702
+Q4_K_M	380	15.4428			0.11637
+Q5_K_S	394	15.5266			0.11682
+Q5_1	400	15.4875			0.11641
+Q5_K_M	401	15.4788			0.11631
+Q6_K	483	15.2145			0.11422
+Q8_0	507	15.2390			0.11452
+F16	949	15.1819			0.11400