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.
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