This repository implements a transformer model with a focus on Multi-Head Latent Attention (MLA), a novel attention mechanism designed to enhance the performance of transformer-based architectures. The project is modular, with components for data preparation, tokenization, model training, and evaluation.
Multi-Head Latent Attention (MLA) is a custom attention mechanism that extends the traditional multi-head attention by incorporating latent representations. This approach allows the model to capture more nuanced dependencies in the input data, improving its ability to handle complex tasks. The implementation is provided in the MultiHeadLatentAttention class within attention.py.
The transformer architecture is the backbone of this project, consisting of modular components such as attention mechanisms, feed-forward networks, and positional encodings. These components are combined into transformer blocks, which are implemented in block.py. The architecture is designed to be flexible and scalable, making it suitable for a wide range of natural language processing tasks.
The feed-forward network is a key component of the transformer architecture, responsible for processing the output of the attention mechanism. It is implemented as a modular layer in feed_forward_net.py, allowing for easy customization and integration into the transformer blocks.
Tokenization is a critical preprocessing step in transformer-based models. This project uses GPT-2 Byte Pair Encoding (BPE) for tokenization, implemented using the tiktoken library in tokenizer.py. The tokenization process ensures that the input text is converted into a format suitable for the model.
The data preparation pipeline is designed to handle raw text data, tokenize it, and split it into training and validation datasets. This process is implemented in data_prepare.py, ensuring that the data is ready for model training.
The model's architecture and hyperparameters are defined in model_config.py. This configuration file allows users to easily modify parameters such as the number of layers, hidden dimensions, and attention heads, enabling experimentation with different model setups.
The training pipeline is implemented in train.py, which handles the training process, including data loading, model optimization, and checkpointing. The evaluation script in test.py provides tools for assessing the model's performance on validation data.
The MLA-Based Transformer is designed for a variety of natural language processing tasks, including text generation, classification, and sequence-to-sequence tasks. Its modular design and custom attention mechanism make it a powerful tool for researchers and practitioners in the field of machine learning.
- Attention Is All You Need: The foundational paper introducing the transformer architecture.
- GPT-2 Byte Pair Encoding: The tokenization method used in this project.
- DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model: DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference
- [Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs] (https://arxiv.org/abs/2502.14837): Multi-head Latent Attention (MLA) is an innovative architecture proposed by DeepSeek, designed to ensure efficient and economical inference by significantly compressing the Key-Value (KV) cache into a latent vector