The hub also presents a roadmap for performing experiments on various LLMs, including Flan-T5, Falcon, and RedPajama, among others. It encourages contributions from users through the "fork-and-pull" Git workflow. The hub's experiments were conducted on the AWS EC2 instance: g5.2xlarge, which has a 24GB Nvidia GPU, sufficient for fine-tuning the LLMs in the repository.
Key takeaways:
- The LLM Finetuning Hub provides code and insights for fine-tuning various large language models (LLMs) for specific use-cases, and evaluates both open-source and closed-source LLMs for real-life business applications.
- The Evaluation Framework used for evaluation consists of four pillars, and the hub provides scripts for fine-tuning LLMs on proprietary datasets and performing hyperparameter optimization.
- The hub provides a step-by-step guide to fine-tuning LLMs, including setting up the environment, installing packages, fine-tuning the chosen LLM, and applying zero-shot and few-shot learning.
- The LLM roadmap lists the LLMs that the hub aims to cover, and the project is open to contributions following the "fork-and-pull" Git workflow.