This repository contains an example project for model-based text generation using the Hugging Face Transformers library with a Causal Language Model (CLM). The project demonstrates how to load a pre-trained CLM and use it to generate continuous text based on input prompts.
Before running the code, make sure to have the following dependencies installed:
Python (recommended version 3.x)
PyTorch (version corresponding to your CUDA version if available)
Hugging Face Transformers library
Load Model and Tokenizer: The example uses a pre-trained model (gpt2-xl) and its corresponding tokenizer. The model will be automatically downloaded from the Hugging Face model hub.
Text Generation: The code demonstrates how to use the loaded model to generate text predictions. The input is used as part of a prompt-based text generation process.
Replace Model: You can replace the model with another one available in the Hugging Face model hub, e.g., gpt2, gpt2-medium, gpt2-large, etc.
Adjust Parameters: Experiment with different model parameters like max_length, num_return_sequences, and num_beams to control text generation.
This project is available under the MIT License.