LLaMA-LoRA-Tuner

LLaMA-LoRA Tuner: Simplifying Model Fine-Tuning with LoRA

LLaMA-LoRA Tuner is an innovative project aimed at providing an easy-to-use platform for fine-tuning and evaluating LLaMA models using a method known as low-rank adaptation (LoRA). Designed to be user-friendly, the project promises a smooth experience whether you're assessing various models or conducting fine-tuning operations.

Key Features

User Interface and Demonstrations

To showcase its capabilities, LLaMA-LoRA Tuner provides a demo on Hugging Face. However, for a more interactive experience, including training and text generation, users are encouraged to try the platform on Google Colab. Here's what makes it stand out:

• Google Colab Integration: Users can quickly get started with a single click, utilizing Google's infrastructure to handle data loading and storage via Google Drive.
• Model Evaluation: The platform allows users to evaluate a variety of LLaMA LoRA models either stored in personal folders or accessible on Hugging Face.
• Model Variety: It supports various base models such as decapoda-research/llama-7b-hf, nomic-ai/gpt4all-j, and others, making it versatile and adaptable.
• Fine-Tuning Capability: Users can fine-tune models using different prompt templates and data formats, supporting widely recognized datasets like Stanford Alpaca.

Support for Various Data Formats

LLaMA-LoRA Tuner supports seamless integration of various data formats. Users can load datasets in JSON and JSONL formats or enter plain text directly into the user interface. By supporting formats such as Stanford Alpaca's seed_tasks and OpenAI's prompt-completion, the project ensures compatibility with popular datasets.

Getting Started

LLaMA-LoRA Tuner offers flexible deployment options to cater to different user needs:

• Google Colab: The easiest method requiring just a Google account. Users can experience the basic functionalities though performance may be limited compared to more advanced setups.
• Cloud Services with SkyPilot: For those with cloud service accounts on platforms like AWS or Azure, SkyPilot facilitates running the app on these services, ensuring data preservation via cloud buckets.
• Local Setup: Users with necessary hardware can run the project locally, offering full control over configurations and operations.

Running on Google Colab

Getting the app started on Google Colab is straightforward:

1. Open the provided Colab notebook and select "Run All".
2. Authorize access to Google Drive for data storage.
3. The setup takes about 5 minutes, after which a public URL will be generated to access the user interface.

Cloud Deployment with SkyPilot

For users opting to run the app on cloud services, SkyPilot provides an efficient way to deploy:

1. Create a task definition using a .yaml file, specifying resources and setup.
2. Launch the task, which will generate a public URL for accessing the app.
3. Easily monitor task status and logs, ensuring smooth operations.

Local Implementation

Setting up the project locally involves preparing the environment with Conda and running straightforward commands to initiate the app, giving users a URL for access.

Additional Features and Usage

The project supports UI development mode, allowing users to test interface elements without loading the full language model. This feature helps in refining user experience without resource constraints.

Acknowledgements

LLaMA-LoRA Tuner builds on efforts by other projects such as alpaca-lora and simple-llama-finetuner, acknowledging their contributions to the field of model fine-tuning.

This project offers an accessible, robust solution for model evaluation and adaptation, catering to both beginners and more advanced users interested in machine learning and natural language processing.