This project provides an experimental pipeline for fine-tuning task-specific language models, abstracting away the complexities of dataset generation, formatting, and model training. It targets users who want to easily create performant, custom AI models from a simple task description, supporting fine-tuning for LLaMA 2 and GPT-3.5.

How It Works

The pipeline leverages large language models (Claude 3, GPT-4, or GPT-3.5) to automate the entire fine-tuning process. It begins by generating a custom dataset of prompts and responses based on a user-provided task description and desired parameters (temperature, number of examples). Subsequently, it generates an effective system prompt, splits the dataset for training and validation, and fine-tunes a chosen base model (LLaMA 2 or GPT-3.5).

Quick Start & Requirements

• Open the provided notebook in Google Colab or a local Jupyter environment.
• Ensure a GPU is available (Colab: Runtime -> Change runtime type).
• Set your OpenAI API key.
• Define task description, temperature, and number of examples in the notebook.
• Run all cells; fine-tuning can take 10 minutes to several hours.
• Official quick-start and usage details are within the notebook.

Highlighted Details

• Automates dataset generation using LLMs (Claude 3, GPT-4).
• Generates effective system prompts for fine-tuned models.
• Supports fine-tuning of LLaMA 2 and GPT-3.5 models.
• Simplifies the end-to-end process from task description to trained model.

Maintenance & Community

• Contributions are welcomed, with suggestions for improving efficiency, diversity of examples, hyperparameter tuning, and model selection.
• Mentions Maxime Labonne for training code.
• Contact: Matt Shumer (@mattshumer_)

Licensing & Compatibility

• MIT License.
• Permissive for commercial use and integration with closed-source projects.

Limitations & Caveats

The project is described as experimental, and performance may vary. The cost of using LLM APIs for dataset generation is a consideration. Fine-tuning times are dependent on dataset size and hardware.