Instructions to use zmail-tech/ZPT-Commit-1.2b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zmail-tech/ZPT-Commit-1.2b-instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="zmail-tech/ZPT-Commit-1.2b-instruct",
	filename="LFM2.5-1.2B-Instruct.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use zmail-tech/ZPT-Commit-1.2b-instruct 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 zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0
# Run inference directly in the terminal:
llama cli -hf zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0
# Run inference directly in the terminal:
llama cli -hf zmail-tech/ZPT-Commit-1.2b-instruct:Q8_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 zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf zmail-tech/ZPT-Commit-1.2b-instruct:Q8_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 zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0

Use Docker

docker model run hf.co/zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0

LM Studio
Jan
Ollama
How to use zmail-tech/ZPT-Commit-1.2b-instruct with Ollama:
```
ollama run hf.co/zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0
```

Unsloth Studio

How to use zmail-tech/ZPT-Commit-1.2b-instruct 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 zmail-tech/ZPT-Commit-1.2b-instruct 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 zmail-tech/ZPT-Commit-1.2b-instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zmail-tech/ZPT-Commit-1.2b-instruct to start chatting

How to use zmail-tech/ZPT-Commit-1.2b-instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0

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": "zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zmail-tech/ZPT-Commit-1.2b-instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zmail-tech/ZPT-Commit-1.2b-instruct:Q8_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 zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use zmail-tech/ZPT-Commit-1.2b-instruct with Docker Model Runner:
```
docker model run hf.co/zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0
```

Lemonade

How to use zmail-tech/ZPT-Commit-1.2b-instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zmail-tech/ZPT-Commit-1.2b-instruct:Q8_0

Run and chat with the model

lemonade run user.ZPT-Commit-1.2b-instruct-Q8_0

List all available models

lemonade list

ZPT-Commit-1.2b-Instruct : Git Commit Message Generator

🚀 Overview

ZPT-Commit-1.2B-Instruct is a highly specialized, 1.2 Billion parameter language model engineered specifically to automate and improve the creation of descriptive and professional Git commit messages.

Fine-tuned from the robust LiquidAI LFM 2.5 1.2b base model, this model uses the Tavernari/git-commit-message-dt dataset to excel at analyzing code diffs and transforming technical changes into clear, actionable commit summaries. It is designed to streamline development workflows by ensuring a clean, searchable, and informative git history.

🛠️ Technical Specifications

This model is provided in highly optimized formats for maximum efficiency across various hardware.

Feature	Specification	Details
Model Name	ZPT-Commit-1.2B-Instruct	Specialized version for generating precise commit messages.
Base Model	LiquidAI LFM 2.5 1.2b	The foundational architecture.
Training Data	`Tavernari/git-commit-message-dt`	Dataset used to fine-tune the model on real-world code changes and commit patterns.
Fine-tuning Framework	Unsloth Studio	Trained 2x faster using the Unsloth optimization techniques.
Model Size	1.2 Billion Parameters	Offers strong performance while maintaining a manageable footprint for deployment.
Supported Formats	GGUF, Quantized	Optimized for CPU/GPU inference via `llama.cpp`.

⚙️ Performance & Implementation

The model has been converted and optimized to the GGUF format, allowing for highly efficient local deployment without needing massive GPU resources.

Performance Benefits:

Unsloth Optimization: The training process utilized Unsloth, enabling faster training cycles and an efficient model structure.
GGUF Efficiency: Quantized files ensure excellent inference speed and lower memory usage, making it ideal for CI/CD pipelines or local development environments.

Available Model Files: The following optimized file is available for immediate use:

LFM2.5-1.2B-Instruct.Q8_0.gguf

💡 Use Cases

ZPT-Commit-1.2B-Instruct is designed to be a powerful aid in the software development lifecycle.

Automated Commit History: Automatically generating high-quality, structured commit messages directly from code diffs.
Developer Workflow Integration: Integrating into IDE extensions or CI/CD tools to enforce consistent commit message standards.
Code Review Enhancement: Providing context-rich summaries of changes, speeding up the review process.
Clean Repository Management: Maintaining a professional and easily traceable version control history.

💻 Installation & Usage

The model is designed to work seamlessly with the llama.cpp ecosystem.

Dependencies:

llama.cpp (for CPU/GPU inference)
unsloth (for initial conversion and optimization)

CLI Usage: You can invoke the model using the following command structure. The model will require the code diff as input for the best results.

Text-Only LLMs:

llama-cli -hf zmail-tech/ZPT-Commit-1.2B-Instruct --jinja

Multimodal Models:

llama-mtmd-cli -hf zmail-tech/ZPT-Commit-1.2B-Instruct --jinja

Note: The model is named Commit as its primary and specialized function is the generation of Git commit messages. For full setup instructions, please refer to the Unsloth AI GitHub resources.

Downloads last month: 102

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

8-bit

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