This project demonstrates how to fine-tune a pre-trained GPT-2 model on a custom text dataset. Fine-tuning a language model like GPT-2 can be useful for generating text that aligns with the style and content of the provided dataset. This project covers the entire process, from setting up the environment and preparing the data to training the model and generating text.
- Project Description
- Model Architecture
- Setup and Installation
- Data Preparation
- Training the Model
- Generating Text
- Results
- License
The goal of this project is to fine-tune a GPT-2 model on a custom dataset to create a specialized language model. This model can generate text that mimics the style and content of the training data. The project involves the following steps:
- Setting Up the Environment: Installing necessary libraries and dependencies.
- Data Preparation: Formatting and loading the custom dataset for training.
- Fine-Tuning the Model: Training the GPT-2 model on the custom dataset.
- Generating Text: Using the fine-tuned model to generate text.
GPT-2 (Generative Pre-trained Transformer 2) is an open-source language model developed by OpenAI. It is a transformer-based model with the following key features:
- Transformer Architecture: GPT-2 uses the transformer architecture, which includes self-attention mechanisms to process input sequences. This architecture is highly effective for various natural language processing tasks.
- Pre-trained on Large Corpus: GPT-2 is pre-trained on a diverse and extensive dataset, enabling it to generate coherent and contextually relevant text.
- Fine-Tuning: The model can be fine-tuned on specific datasets to adapt its output style and content to match the training data.
- Layers: GPT-2 consists of multiple transformer layers. The number of layers can vary depending on the specific GPT-2 variant used (e.g., GPT-2 small, medium, large).
- Attention Heads: Each layer has multiple attention heads, allowing the model to focus on different parts of the input sequence simultaneously.
- Feedforward Networks: Each transformer layer includes feedforward networks for processing the attention outputs.
- Positional Encoding: GPT-2 uses positional encodings to retain the order of tokens in the input sequence.
To set up the environment and install the required libraries, follow the llm_env create folder.
Prepare your custom dataset by ensuring it is in a suitable format for training. Each line in the dataset should be a separate training example. Save the dataset in a text file, e.g., data.txt.
Fine-tuning the GPT-2 model involves the following steps:
- Load the Pre-trained Model and Tokenizer: Initialize the GPT-2 model and tokenizer from the
transformerslibrary. - Set the Padding Token: Ensure the tokenizer uses a padding token.
- Load the Dataset: Use the
datasetslibrary to load and tokenize the custom dataset. - Define Training Arguments: Set up training parameters such as batch size, number of epochs, and output directory.
- Create a Trainer: Use the
Trainerclass from thetransformerslibrary to manage the training loop. - Train the Model: Fine-tune the GPT-2 model on the custom dataset.
- Save the Model and Tokenizer: Save the fine-tuned model and tokenizer for later use.
After fine-tuning, use the model to generate text:
- Load the Fine-Tuned Model and Tokenizer: Initialize the model and tokenizer from the saved files.
- Encode Input Text: Tokenize the input prompt.
- Generate Text: Use the model to generate text based on the input prompt.
- Decode the Output: Convert the generated token IDs back to text.
After training, the fine-tuned model can generate text that aligns with the style and content of your custom dataset. Here’s an example of text generated by the fine-tuned model:
What is my name? Harsh Anand. In my next blog, I am working on LLM.