Instructions to use seniruk/commitGen-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use seniruk/commitGen-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 seniruk/commitGen-gguf # Run inference directly in the terminal: llama cli -hf seniruk/commitGen-gguf
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf seniruk/commitGen-gguf # Run inference directly in the terminal: llama cli -hf seniruk/commitGen-gguf
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/commitGen-gguf # Run inference directly in the terminal: ./llama-cli -hf seniruk/commitGen-gguf
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/commitGen-gguf # Run inference directly in the terminal: ./build/bin/llama-cli -hf seniruk/commitGen-gguf
Use Docker
docker model run hf.co/seniruk/commitGen-gguf
- LM Studio
- Jan
- Ollama
How to use seniruk/commitGen-gguf with Ollama:
ollama run hf.co/seniruk/commitGen-gguf
- Unsloth Studio
How to use seniruk/commitGen-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 seniruk/commitGen-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 seniruk/commitGen-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for seniruk/commitGen-gguf to start chatting
- Pi
How to use seniruk/commitGen-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf seniruk/commitGen-gguf
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/commitGen-gguf" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use seniruk/commitGen-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 seniruk/commitGen-gguf
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/commitGen-gguf
Run Hermes
hermes
- OpenClaw new
How to use seniruk/commitGen-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf seniruk/commitGen-gguf
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 "seniruk/commitGen-gguf" \ --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"
- Docker Model Runner
How to use seniruk/commitGen-gguf with Docker Model Runner:
docker model run hf.co/seniruk/commitGen-gguf
- Lemonade
How to use seniruk/commitGen-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull seniruk/commitGen-gguf
Run and chat with the model
lemonade run user.commitGen-gguf-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
| license: apache-2.0 | |
| # 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 | |
| [](https://huggingface.co/seniruk) | |
| [](https://medium.com/@senirukepasinghe) | |
| [](https://www.linkedin.com/in/seniru-epasinghe-b34b86232/) | |
| [](https://github.com/seth2k2) | |
| # Purpose | |
| Used for generating high quality commit messages for a given git difference | |
| ### Model Description | |
| Generated by fine tuning Qwen2.5-Coder-1.5B-Instruct on bigcode/commitpackft dataset for 2 epochs | |
| Trained on a total of 277 Languages | |
| Achieved a final training loss in the range of 1- 1.7 (due to data set not containing equal data rows for each language) | |
| For common languages(python, java ,javascripts,c etc) loss went for a minimum of 1.0335 | |
| ## Environmental Impact | |
| - **Hardware Type:** geforce RTX 4060 TI - 16GB] | |
| - **Hours used:** 10 Hours | |
| - **Cloud Provider:** local | |
| ### Results | |
|  | |
|  | |
| ### Inference | |
| ```python | |
| from llama_cpp import Llama | |
| llm = Llama.from_pretrained( | |
| repo_id="seniruk/commitGen-gguf", | |
| filename="commitGen.gguf", | |
| ) | |
| diff="" #the git difference | |
| instruction= "" #the instruction --> 'create a commit message for given git difference' | |
| prompt = "{}{}".format(instruction,diff) | |
| messages = [ | |
| {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| output = llm.create_chat_completion( | |
| messages=messages, | |
| temperature=0.5 | |
| ) | |
| llm_message = output['choices'][0]['message']['content'] | |
| print(llm_message) | |
| ``` |