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
- llama-cpp-python
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
- Pi
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
- OpenClaw new
How to use zmail-tech/ZPT-Commit-1.2b-instruct with OpenClaw:
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 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 "zmail-tech/ZPT-Commit-1.2b-instruct:Q8_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"
- 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
Update README.md
Browse filesInitial version of model card
README.md
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---
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# ZPT-Commit-1.2b-
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**
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- For text only LLMs: `llama-cli -hf zmail-tech/ZPT-Commit-1.2b-instruct --jinja`
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- For multimodal models: `llama-mtmd-cli -hf zmail-tech/ZPT-Commit-1.2b-instruct --jinja`
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---
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# ZPT-Commit-1.2b-Instruct : Git Commit Message Generator
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## 🚀 Overview
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**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.
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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.
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---
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## 🛠️ Technical Specifications
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This model is provided in highly optimized formats for maximum efficiency across various hardware.
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| Feature | Specification | Details |
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| :--- | :--- | :--- |
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| **Model Name** | ZPT-Commit-1.2B-Instruct | Specialized version for generating precise commit messages. |
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| **Base Model** | LiquidAI LFM 2.5 1.2b | The foundational architecture. |
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| **Training Data** | `Tavernari/git-commit-message-dt` | Dataset used to fine-tune the model on real-world code changes and commit patterns. |
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| **Fine-tuning Framework** | Unsloth Studio | Trained 2x faster using the Unsloth optimization techniques. |
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| **Model Size** | 1.2 Billion Parameters | Offers strong performance while maintaining a manageable footprint for deployment. |
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| **Supported Formats** | GGUF, Quantized | Optimized for CPU/GPU inference via `llama.cpp`. |
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---
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## ⚙️ Performance & Implementation
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The model has been converted and optimized to the **GGUF** format, allowing for highly efficient local deployment without needing massive GPU resources.
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**Performance Benefits:**
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* **Unsloth Optimization:** The training process utilized Unsloth, enabling faster training cycles and an efficient model structure.
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* **GGUF Efficiency:** Quantized files ensure excellent inference speed and lower memory usage, making it ideal for CI/CD pipelines or local development environments.
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**Available Model Files:**
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The following optimized file is available for immediate use:
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* `LFM2.5-1.2B-Instruct.Q8_0.gguf`
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---
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## 💡 Use Cases
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ZPT-Commit-1.2B-Instruct is designed to be a powerful aid in the software development lifecycle.
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* **Automated Commit History:** Automatically generating high-quality, structured commit messages directly from code diffs.
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* **Developer Workflow Integration:** Integrating into IDE extensions or CI/CD tools to enforce consistent commit message standards.
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* **Code Review Enhancement:** Providing context-rich summaries of changes, speeding up the review process.
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* **Clean Repository Management:** Maintaining a professional and easily traceable version control history.
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---
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## 💻 Installation & Usage
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The model is designed to work seamlessly with the `llama.cpp` ecosystem.
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**Dependencies:**
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* `llama.cpp` (for CPU/GPU inference)
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* `unsloth` (for initial conversion and optimization)
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**CLI Usage:**
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You can invoke the model using the following command structure. The model will require the code diff as input for the best results.
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* **Text-Only LLMs:**
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```bash
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llama-cli -hf zmail-tech/ZPT-Commit-1.2B-Instruct --jinja
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```
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* **Multimodal Models:**
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```bash
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llama-mtmd-cli -hf zmail-tech/ZPT-Commit-1.2B-Instruct --jinja
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```
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---
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***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](https://github.com/unslothai/unsloth) resources.*
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