🚀 Welcome to the Personal LLM project! This repository contains a Jupyter Notebook demonstrating various techniques and methodologies for working with Language Models (LM) using PyTorch and Hugging Face Transformers. Below is a detailed breakdown of the contents and functionalities provided in the notebook.
Ensure you have the following dependencies installed:
torchtransformerspandasnumpy
You can install them using:
pip install torch transformers pandas numpy- Loading Data: The notebook includes code snippets for loading and preprocessing text data.
- Tokenization: Utilizes Hugging Face tokenizers to prepare data for model training.
- Model Initialization: Demonstrates initializing a pre-trained model (
T5Modelin this case). - Training Loop: Contains a detailed training loop for fine-tuning the model on custom data.
- CUDA Handling: Explicitly sets
use_cuda=Falseto handle CUDA availability issues.
- Self-Attention: Explains the self-attention mechanism in detail, including masking techniques for autoregressive models.
- Attention Visualization: Visualizes attention weights to understand how the model focuses on different parts of the input.
- Text Generation: Provides examples of generating text using the fine-tuned model.
- Placeholder Prompts: Shows how to generate prompts for placeholders using the trained model.
- Custom Layers: Includes examples of adding custom layers to the model.
- Loss Functions: Discusses different loss functions and their implementations.
- Data Export: Code snippets for exporting results to CSV and JSON formats.
- Visualization: Various visualization techniques to understand model performance.
- Clone the repository:
git clone https://github.com/yourusername/personal-LLM.git
- Navigate to the project directory:
cd personal-LLM - Open the Jupyter Notebook:
jupyter notebook personal-LLM.ipynb
Feel free to contribute to this project by opening issues or submitting pull requests. Let's build something amazing together! 🌟
This project is licensed under the MIT License.
Happy coding! 🎉