Instructions to use seniruk/qwen2.5coder-0.5B_commit_msg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use seniruk/qwen2.5coder-0.5B_commit_msg with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="seniruk/qwen2.5coder-0.5B_commit_msg",
	filename="qwen0.5-finetuned.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 seniruk/qwen2.5coder-0.5B_commit_msg with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf seniruk/qwen2.5coder-0.5B_commit_msg
# Run inference directly in the terminal:
llama-cli -hf seniruk/qwen2.5coder-0.5B_commit_msg

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf seniruk/qwen2.5coder-0.5B_commit_msg
# Run inference directly in the terminal:
llama-cli -hf seniruk/qwen2.5coder-0.5B_commit_msg

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 seniruk/qwen2.5coder-0.5B_commit_msg
# Run inference directly in the terminal:
./llama-cli -hf seniruk/qwen2.5coder-0.5B_commit_msg

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 seniruk/qwen2.5coder-0.5B_commit_msg
# Run inference directly in the terminal:
./build/bin/llama-cli -hf seniruk/qwen2.5coder-0.5B_commit_msg

Use Docker

docker model run hf.co/seniruk/qwen2.5coder-0.5B_commit_msg

LM Studio
Jan

vLLM

How to use seniruk/qwen2.5coder-0.5B_commit_msg with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "seniruk/qwen2.5coder-0.5B_commit_msg"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "seniruk/qwen2.5coder-0.5B_commit_msg",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/seniruk/qwen2.5coder-0.5B_commit_msg

Ollama
How to use seniruk/qwen2.5coder-0.5B_commit_msg with Ollama:
```
ollama run hf.co/seniruk/qwen2.5coder-0.5B_commit_msg
```

Unsloth Studio new

How to use seniruk/qwen2.5coder-0.5B_commit_msg 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 seniruk/qwen2.5coder-0.5B_commit_msg 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 seniruk/qwen2.5coder-0.5B_commit_msg to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for seniruk/qwen2.5coder-0.5B_commit_msg to start chatting

Pi new

How to use seniruk/qwen2.5coder-0.5B_commit_msg with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf seniruk/qwen2.5coder-0.5B_commit_msg

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": "seniruk/qwen2.5coder-0.5B_commit_msg"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use seniruk/qwen2.5coder-0.5B_commit_msg with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf seniruk/qwen2.5coder-0.5B_commit_msg

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 seniruk/qwen2.5coder-0.5B_commit_msg

Run Hermes

hermes

Docker Model Runner
How to use seniruk/qwen2.5coder-0.5B_commit_msg with Docker Model Runner:
```
docker model run hf.co/seniruk/qwen2.5coder-0.5B_commit_msg
```

Lemonade

How to use seniruk/qwen2.5coder-0.5B_commit_msg with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull seniruk/qwen2.5coder-0.5B_commit_msg

Run and chat with the model

lemonade run user.qwen2.5coder-0.5B_commit_msg-{{QUANT_TAG}}

List all available models

lemonade list

Hi, I’m Seniru Epasinghe 👋

I’m an AI undergraduate and an AI enthusiast, working on machine learning projects and open-source contributions.
I enjoy exploring AI pipelines, natural language processing, and building tools that make development easier.

🌐 Connect with me

Finetuned-qwen2.5-coder-0.5B model on 100000 rows of a cutom dataset containing. git-differences and respective commit messages

Each row of the dataset was formatted as below to suit finetuning requirement of Qwen2.5-coder model so we have to use the same prompt for better results

"""Generate a concise and meaningful commit message based on the provided Git diff.

### Git Diff:
{Git diff from dataset}

### Commit Message:"""

Code for inference of the gguf model is given below

from llama_cpp import Llama

modelGGUF = Llama.from_pretrained(
    repo_id="seniruk/qwen2.5coder-0.5B_commit_msg",
    filename="qwen0.5-finetuned.gguf",
    rope_scaling={"type": "linear", "factor": 2.0},
    chat_format=None,  # Disables any chat formatting
    n_ctx=32768,  # Set the context size explicitly
)

# Define the commit message prompt (Minimal format, avoids assistant behavior)
commit_prompt = """Generate a meaningful commit message explaining all the changes in the provided Git diff.

### Git Diff:
{}

### Commit Message:"""  # Removed {} after "Commit Message:" to prevent pre-filled text.

# Git diff example for commit message generation
git_diff_example = """
diff --git a/index.html b/index.html
index 89abcde..f123456 100644
--- a/index.html
+++ b/index.html
@@ -5,16 +5,6 @@ <body>
     <h1>Welcome to My Page</h1>

-    <table border="1">
-        <tr>
-            <th>Name</th>
-            <th>Age</th>
-        </tr>
-        <tr>
-            <td>John Doe</td>
-            <td>30</td>
-        </tr>
-    </table>

+    <p>This is a newly added paragraph replacing the table.</p>
 </body>
</html>
"""

# Prepare the raw input prompt
input_prompt = commit_prompt.format(git_diff_example)

# Generate commit message
output = modelGGUF(
    input_prompt,
    max_tokens=64,
    temperature=0.6, # Balanced randomness
    top_p=0.8,      # Controls nucleus sampling
    top_k=50,       # Limits vocabulary selection
)

# Decode and print the output
commit_message = output["choices"][0]["text"].strip()

print("\nGenerated Commit Message:\n{}".format(commit_message))

Downloads last month: 14

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for seniruk/qwen2.5coder-0.5B_commit_msg

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B

Quantized

(37)

this model

seniruk
/

qwen2.5coder-0.5B_commit_msg