https://github.com/madhanmohanreddy2301/gemma-instruct-2b-finetuning-on-alpaca

This project demonstrates the steps required to fine-tune the Gemma model for tasks like code generation. We use qLora quantization to reduce memory usage and the SFTTrainer from the trl library for supervised fine-tuning.
https://github.com/madhanmohanreddy2301/gemma-instruct-2b-finetuning-on-alpaca

fine-tuning gemma-2b in llm llm-training pretrained-models

Last synced: 2 days ago
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This project demonstrates the steps required to fine-tune the Gemma model for tasks like code generation. We use qLora quantization to reduce memory usage and the SFTTrainer from the trl library for supervised fine-tuning.

• Host: GitHub
• URL: https://github.com/madhanmohanreddy2301/gemma-instruct-2b-finetuning-on-alpaca
• Owner: MadhanMohanReddy2301
• License: mit
• Created: 2024-06-30T06:59:33.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-30T07:10:37.000Z (5 months ago)
• Last Synced: 2024-07-01T13:37:09.598Z (5 months ago)
• Topics: fine-tuning, gemma-2b, in, llm, llm-training, pretrained-models
• Language: Jupyter Notebook
• Homepage:
• Size: 104 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Fine-tuning Gemma using qLora and Supervised Fine-tuning

This repository contains a comprehensive notebook and tutorial on how to fine-tune the `gemma-7b-it` model using qLora and Supervised Fine-tuning.

## Overview

This project demonstrates the steps required to fine-tune the Gemma model for tasks like code generation. We use qLora quantization to reduce memory usage and the `SFTTrainer` from the `trl` library for supervised fine-tuning.

## Notebook

The notebook is available on my GitHub:

[gemma-Instruct-2b-Finetuning-on-alpaca.ipynb](https://github.com/MadhanMohanReddy2301/gemma-Instruct-2b-Finetuning-on-alpaca/blob/main/gemma-Instruct-Finetuning-on-alpaca.ipynb)

## Prerequisites

Before running the notebook, ensure you have the following:

1. **GPU**: 

   - [gemma-2b](https://huggingface.co/google/gemma-2b) can be fine-tuned on a T4 GPU (free on Google Colab).

   - [gemma-7b](https://huggingface.co/google/gemma-7b) requires an A100 GPU.

2. **Python Packages**: Install the necessary packages using the following commands:

   ```sh

   !pip3 install -q -U bitsandbytes==0.42.0 peft==0.8.2 trl==0.7.10 accelerate==0.27.1 datasets==2.17.0 transformers==4.38.0

   ```