Gemma LLM Fine-tuning

This project refines the capabilities of Gemma, a large language model (LLM), to excel at question-answering tasks on the OpenOrca dataset. By leveraging supervised fine-tuning techniques, the project enhances Gemma’s (both 2B and 7B parameter versions) ability to process instructions delivered in the GPT-4 format and answer questions effectively. To achieve this, the project employs Transformer Reinforcement Learning, a powerful method for fine-tuning LLMs.

Completion Date: Feb 2024 | Tools: Torch, HuggingFace, Gemma LLM, Accelerate , TRL, Link

  • Goal:
    • Improve Gemma LLM’s performance in question-answering tasks using the OpenOrca dataset.
    • Specifically, the project focuses on fine-tuning Gemma to handle instructions provided in the GPT-4 format.
  • Methodology:
    • Supervised Fine-tuning: The project utilizes supervised fine-tuning techniques to tailor Gemma (2B and 7B parameter versions) for the target task. This involves training Gemma on question-and-answer pairs from the OpenOrca dataset while incorporating GPT-4 instruction formats.
    • Transformer Reinforcement Learning: The project employs Transformer Reinforcement Learning, an advanced technique that rewards Gemma for providing accurate answers based on the GPT-4 instructions and the OpenOrca dataset. This approach reinforces desirable behaviors in the LLM, leading to improved performance.
  • Technical Stack:
    • Hardware: The project leverages the computational power of an AWS g5.12xlarge server to handle the intensive training processes required for fine-tuning a large language model.
    • Software:
      • Hugging Face: A popular library for working with LLMs, likely used to access and manage the Gemma LLM.
      • Accelerate: A library designed to accelerate deep learning workloads, potentially used to optimize training on the AWS server.
      • Torch: A deep learning framework likely used to implement the Transformer Reinforcement Learning algorithm.
      • Trl: Potentially a custom library or toolkit specific to the project’s requirements, although its exact function is unclear without further context.

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