Here’s an updated README.md file including LLaMA 2 for text summarization:

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Summarization using Transformer Models (Including LLaMA 2)

This repository contains code and resources for performing text summarization using various Transformer-based models, including the powerful LLaMA 2 model alongside other models like BERT, DistilBERT, Pegasus, T5, and RoBERTa. The project demonstrates the fine-tuning of these models on the CNN Daily Mail dataset to generate concise and coherent summaries from news articles.

Features

• Fine-tuning of various Transformer models, including LLaMA 2 for text summarization tasks.
• Memory-efficient training using QLoRa and LoRa quantization techniques.
• Detailed evaluation metrics to measure summarization performance.
• Instructions to replicate experiments with custom datasets.

Models Implemented

• LLaMA 2 7b (Large Language Model Meta AI)
• BERT (Bidirectional Encoder Representations from Transformers)
• DistilBERT (A smaller, faster, cheaper version of BERT)
• Pegasus (Pre-training with Extracted Gap-sentences for Abstractive Summarization)
• T5 (Text-To-Text Transfer Transformer)
• RoBERTa (Robustly Optimized BERT Pretraining Approach)

Dataset

The models are fine-tuned using the CNN Daily Mail dataset, which is widely used for text summarization tasks and consists of news articles paired with human-written summaries.

Dataset Structure

• Articles: Long-form news articles.
• Summaries: Short summaries corresponding to each article.

Quantization Techniques

To optimize the training process, the following quantization techniques are employed:

• QLoRa: Quantization-aware LoRa, which reduces memory usage while maintaining performance.
• LoRa: A low-rank adaptation technique that reduces trainable parameters, speeding up training and inference.