Welcome to myLLM! This project is an educational initiative to build a Language Model (LLM) from scratch, exploring various transformer techniques along the way. The goal is to gain a deep understanding of how modern language models work and to experiment with different approaches in the transformer architecture.
- Introduction
- Features
- Installation
- Usage
- Project Structure
- Transformer Techniques
- Contributing
- License
myLLM is designed for educational purposes to understand and implement the core components of language models based on transformer architectures. The project covers:
- Tokenization
- Embeddings
- Encoder-Decoder models
- Attention mechanisms
- Training and fine-tuning techniques
- Custom Tokenizer: Build and train your own tokenizer.
- Embedding Layer: Learn how to create and use embedding layers.
- Transformer Architecture: Implement encoder and decoder components.
- Attention Mechanisms: Explore different attention mechanisms, including self-attention and cross-attention.
- Training Pipeline: Set up a training pipeline for your model.
- Fine-Tuning: Techniques to fine-tune the model on specific tasks.
To get started with myLLM, you need to have Python 3.8 or higher installed. Follow the steps below to set up the project:
-
Clone the repository:
git clone https://github.com/ArshiaZr/myLLM.git cd myLLM -
Create a virtual environment and activate it:
python -m venv cuda source cuda/bin/activate # On Windows, use `cuda\Scripts\activate`
-
Install the required packages:
pip install -r requirements.txt
To train and test your LLM, follow these steps:
-
Tokenization: Tokenize your dataset using the custom tokenizer.
from tokenizer import CustomTokenizer tokenizer = CustomTokenizer() tokenizer.train('path_to_dataset')
-
Model Training: Train the transformer model.
from models.GPT import GPTLanguageModel model = GPTLanguageModel(vocab_size=vocab_size, device=device) model._train(epochs=200, learning_rate=3e-4, eval_iters=100)
-
Inference: Use the trained model for inference.
from utils.helpers import load_model model = load_model('path_to_trained_model') result = model.generate('your_encoded_input_text') print(result)
The project structure is organized as follows:
myLLM/
├── data/
├── models/
│ ├── transformer.py
│ └── ...
├── tokenization/
│ ├── tokenizer.py
│ └── ...
├── utils/
│ ├── helpers.py
│ └── ...
├── README.md
└── requirements.txt
This project explores various techniques in transformer models, including:
- Positional Encoding: Adding positional information to the embeddings.
- Multi-Head Attention: Implementing and understanding multi-head attention mechanisms.
- Layer Normalization: Using layer normalization to stabilize training.
- Feed-Forward Networks: Incorporating feed-forward neural networks within the transformer blocks.
- Residual Connections: Implementing residual connections to improve gradient flow.
Contributions are welcome! If you have suggestions or improvements, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.
Feel free to customize this README to better fit your project's specifics and your preferences!