Fine-tuning LLaMA-2 with Google Colab: a step-by-step tutorial

Introduction

In this tutorial, we will explore LLaMA-2, Meta’s second-generation open-source LLM collection, and demonstrate how to fine-tune it on a new dataset using Google Colab. We will cover new methodologies and fine-tuning techniques, as well as provide a video walk-through of the process.

What is LLaMA-2?

LLaMA-2 is Meta’s second-generation open-source LLM collection and uses an optimized transformer architecture. It offers models in various sizes, making it suitable for a wide range of natural language processing tasks.

Fine-Tuning LLaMA-2

Fine-tuning involves adapting a pre-trained LLM to a specific task or dataset. In this tutorial, we will use Supervised Fine-Tuning (SFT) to fine-tune LLaMA-2 on an example dataset.

Steps Involved

1. Import the required libraries: ```python import transformers from transformers import AutoTokenizer from transformers import AutoModelForSeq2SeqLM ``` 2. Load the tokenizer and model: ```python tokenizer = AutoTokenizer.from_pretrained("meta-ai/llama-large-finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("meta-ai/llama-large-finetuned") ``` 3. Prepare your dataset: Convert your dataset into a format compatible with the model. 4. Create a data loader: Create a data loader to iterate over your dataset during training. 5. Define the fine-tuning hyperparameters: Specify the learning rate, number of epochs, and other training parameters. 6. Fine-tune the model: Use the `Trainer` class from `transformers` to fine-tune the model. 7. Evaluate the fine-tuned model: Assess the performance of the fine-tuned model on a held-out validation set.

Conclusion

This tutorial provides a comprehensive guide to fine-tuning LLaMA-2 with Google Colab. By following the steps outlined, you can leverage the capabilities of LLaMA-2 to improve the performance of your natural language processing tasks.