This repository provides a comprehensive collection of fine-tuning notebooks for various large language models (LLMs), targeting users of Google Colab, Kaggle, and other similar platforms. It simplifies the process of adapting models for specific tasks like conversational AI, text completion, and vision-language understanding, enabling researchers and developers to quickly experiment with and deploy customized LLMs.

How It Works

The project offers pre-configured Jupyter notebooks, each tailored for a specific LLM and fine-tuning task (e.g., GRPO, Alpaca, Conversational). These notebooks abstract away complex setup and dependency management, allowing users to directly run fine-tuning experiments within their chosen cloud environment. The structure facilitates easy navigation and selection of models and fine-tuning methodologies.

Quick Start & Requirements

• Access: Notebooks are directly accessible via "Open in Colab" or "Open in Kaggle" links provided in the README.
• Prerequisites: Requires a Google Colab or Kaggle account with access to GPU resources. Specific model fine-tuning may have additional library requirements detailed within individual notebooks.
• Setup: Minimal setup required, as notebooks are pre-configured for cloud execution environments.

Highlighted Details

• Extensive coverage of popular LLMs including Llama, Mistral, Phi, Gemma, and Qwen.
• Support for diverse fine-tuning techniques such as GRPO, Alpaca, Conversational, DPO, ORPO, and Vision.
• Dedicated notebooks for specialized tasks like Text Completion, Tool Calling, and Text-to-Speech (TTS).
• Includes a contribution guide and template for adding new model fine-tuning notebooks.

Maintenance & Community

The project appears to be actively maintained by the Unsloth AI team, with a clear contribution process outlined. Further community engagement channels are not explicitly listed in the README.

Licensing & Compatibility

The repository itself is not explicitly licensed in the provided README snippet. However, the notebooks are designed to fine-tune models that have their own respective licenses. Users must adhere to the licenses of the underlying LLMs and any datasets used.

Limitations & Caveats

This repository exclusively provides notebooks and does not include the underlying Unsloth library or its optimizations. Users seeking the core Unsloth fine-tuning framework will need to refer to separate Unsloth repositories. The effectiveness of fine-tuning depends on the quality of the provided notebooks and the user's chosen datasets.