https://github.com/ruvenguna94/dialogue-summary-peft-fine-tuning
This notebook fine-tunes the FLAN-T5 model for dialogue summarization, comparing full fine-tuning with Parameter-Efficient Fine-Tuning (PEFT). It evaluates performance using ROUGE metrics, demonstrating PEFT's efficiency while achieving competitive results.
https://github.com/ruvenguna94/dialogue-summary-peft-fine-tuning
dialogue-summarization fine-tuning flan-t5 generative-ai hugging-face lora low-rank-adaptation natural-language-processing nlp parameter-efficient-fine-tuning peft pytorch rouge
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This notebook fine-tunes the FLAN-T5 model for dialogue summarization, comparing full fine-tuning with Parameter-Efficient Fine-Tuning (PEFT). It evaluates performance using ROUGE metrics, demonstrating PEFT's efficiency while achieving competitive results.
- Host: GitHub
- URL: https://github.com/ruvenguna94/dialogue-summary-peft-fine-tuning
- Owner: RuvenGuna94
- Created: 2024-12-24T09:27:18.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-01-04T11:05:06.000Z (5 months ago)
- Last Synced: 2025-02-13T03:54:44.691Z (3 months ago)
- Topics: dialogue-summarization, fine-tuning, flan-t5, generative-ai, hugging-face, lora, low-rank-adaptation, natural-language-processing, nlp, parameter-efficient-fine-tuning, peft, pytorch, rouge
- Language: Jupyter Notebook
- Homepage:
- Size: 289 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Fine-Tuning a Generative AI Model
## Description
This repository contains a Jupyter Notebook titled **PEFT_Fine_Tuning.ipynb**, which provides a step-by-step guide to fine-tuning a generative AI model. The notebook explores techniques to adapt a pre-trained language model to specific tasks or datasets, enhancing its performance and applicability.
## Features
- Introduction to the challenges of evaluating LLMs.
- Comparison of traditional machine learning evaluation methods with those required for LLMs.
- Implementation of key evaluation metrics:
- **ROUGE (Recall-Oriented Understudy for Gisting Evaluation)**
- **BLEU (Bilingual Evaluation Understudy)**
- Demonstrates fine-tuning of generative models on custom datasets.
- Practical examples and calculations for metrics like ROUGE-1, ROUGE-2, and ROUGE-L.
## Prerequisites
To run the notebook, ensure you have the following installed:
- Python 3.8 or later
- Jupyter Notebook or JupyterLab
- Required Python libraries (install using the provided requirements file):
- `transformers`
- `datasets`
- `torch`
- `numpy`
- `scipy`
## Installation
1. Clone the repository:
```bash
git clone [https://github.com/RuvenGuna94/LLM-Fine-Tuning.git](https://github.com/RuvenGuna94/Dialogue-Summary-PEFT-Fine-Tuning.git)
cd LLM-Fine-Tuning
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Open the notebook `PEFT_Fine_Tuning.ipynb` in Jupyter Notebook or VS Code.
2. Follow the steps outlined in the notebook to fine-tune the FLAN-T5 model.
3. Evaluate the model's performance using the ROUGE metric.
## Contributing
Feel free to fork the repository and submit pull requests. Suggestions and improvements are welcome!
## License
This project is licensed under the MIT License. See the LICENSE file for details.
## Developer Log
### 24 December 2024
- Created the repo, directory and all necessary files
- Configured local env to run code
- Installed required conda env and kernel
### 26 December 2024
- Updated all version libraries such that it is able to run
- Load and test base model with zero shot inference
- Fine tuning
- Preprocessed dataset with instructional prompt
- Create train, validation and test sets
- Fine tune with minimum configs to reduce compute requirements
- Evaluate against base model (Human evaluation and ROUGE metric)
- Perform PEFT
- Setup and load LORA
- Create and train PEFT adapter
- Model Evaluation
### 27 December
- Converted print statements for model performance comparison into graphs
- Updated technical explanations for more clarity
- Added in conclusion.