LightweightFineTuning

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Model Description

This project demonstrates lightweight fine-tuning of a pre-trained language model using Parameter-Efficient Fine-Tuning (PEFT) techniques.

• Developed by: Ian Miao
• Model type: GPT-2 with Low-Rank Adaptation (LoRA)
• Language: English
• License: MIT
• Finetuned from model: GPT-2 (base model from Hugging Face)

Model Sources

• Base Model: GPT-2
• PEFT Library: PEFT

Project Overview

This project implements a lightweight fine-tuning approach using Low-Rank Adaptation (LoRA) on the GPT-2 model for sentiment analysis. The Stanford Sentiment Treebank (SST-2) dataset is used for fine-tuning and evaluation.

Key Components

• PEFT Technique: Low-Rank Adaptation (LoRA)
• Base Model: GPT-2 (gpt2)
• Task: Sentiment Analysis
• Dataset: Stanford Sentiment Treebank (SST-2)
• Evaluation Approach: Accuracy metric with Hugging Face's Trainer

How to Get Started with the Model

The implementation is available in the Jupyter notebook LightweightFineTuning.ipynb. To run the notebook:

1. Ensure you have the required dependencies installed:

   pip install transformers datasets evaluate peft torch accelerate
   

2. Open and run the notebook to see the implementation details, training process, and evaluation results.

Training Details

Training Data

The model is fine-tuned on the Stanford Sentiment Treebank (SST-2) dataset, which contains movie reviews labeled with binary sentiment (positive/negative).

Training Procedure

The training uses LoRA, which adds trainable rank decomposition matrices to existing weights while freezing the original model parameters. This significantly reduces the number of trainable parameters compared to full fine-tuning.

Training Hyperparameters

• PEFT Method: LoRA
• Rank: 8
• Alpha: 16
• Dropout: 0.1
• Training Regime: Mixed precision (fp16)

Evaluation

Metrics

The model is evaluated using accuracy on the SST-2 validation set.

Results

The fine-tuned model demonstrates improved performance on sentiment analysis compared to the base model, while requiring significantly fewer trainable parameters.

Technical Specifications

Compute Infrastructure

• The model was trained using PyTorch and the Hugging Face Transformers and PEFT libraries.

Framework Versions

• PEFT 0.14.0
• Transformers (Hugging Face)
• PyTorch
• Datasets (Hugging Face)

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