Fine-tuning a GPT-2 Language Model on the Alpaca Dataset for Text Generation

https://www.it-jim.com/blog/training-and-fine-tuning-gpt-2-and-gpt-3-models-using-hugging-face-transformers-and-openai-api/

In this article, we’ll explore how to fine-tune a pre-trained GPT-2 language model on the Alpaca dataset using the Hugging Face Transformers library. The goal is to train the model to generate coherent and contextually relevant text based on the input provided.

Introduction to GPT-2 and the Alpaca Dataset

GPT-2 (Generative Pre-trained Transformer 2) is a state-of-the-art language model developed by OpenAI. It is capable of generating human-like text based on the input it receives. The Alpaca dataset, on the other hand, is a large and diverse text dataset that can be used for training language models.

Hands on

Setting Up the Environment

To get started, we need to install the Transformers library:

pip install transformers

Next, we’ll load the pre-trained GPT-2 model and tokenizer:

from transformers import GPT2Tokenizer, GPT2LMHeadModel

model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

This part of the code is used to initialize a pre-trained GPT-2 tokenizer and language model using the…