SuperAdapters provides a unified framework for fine-tuning a wide range of Large Language Models (LLMs) using various adapter techniques. It aims to simplify the process of adapting LLMs for diverse tasks and hardware, catering to researchers and developers working with LLMs.

How It Works

The library supports multiple adapter methods including LoRA, QLoRA, AdaLoRA, Prefix Tuning, P-Tuning, and Prompt Tuning. It offers a flexible architecture that allows users to select specific LLM architectures (e.g., LLaMA, ChatGLM, Qwen, Mixtral) and apply different fine-tuning strategies. The framework handles data loading from files or databases and supports both sequence-to-sequence and classification tasks.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python 3.10.0 recommended for macOS with XZ support. GPU support requires specific PyTorch nightly builds for macOS.
• Data: Supports data from files or MySQL databases.
• Models: Links to Hugging Face model repositories are provided for various LLMs.
• Usage: Examples for fine-tuning and inference with different models and adapters are available. See Usage Examples.

Highlighted Details

• Supports fine-tuning on Windows, Linux, and Mac M1/2.
• Offers a unified interface for multiple adapter types and LLM architectures.
• Includes tools for combining base models with adapter weights for easier deployment.
• Provides options for web-based demos and API endpoints for inference.

Maintenance & Community

The project references other repositories like LLM-Adapters and Alpaca-CoT, suggesting community engagement and potential for shared development. No specific community channels or active maintainer information is detailed in the README.

Licensing & Compatibility

The README does not explicitly state a license. Given the references to other open-source projects, it is likely to be permissively licensed, but this requires verification. Compatibility for commercial use is not specified.

Limitations & Caveats

QLoRA is not supported on Mac. Users on macOS may need to recompile Python with XZ support for full functionality. The README does not detail performance benchmarks or specific hardware requirements beyond general OS and GPU considerations.