Building a Language Model from Scratch

Introduction

In this project, I embarked on building a language model from scratch using resources provided by Andrej Karpathy. The goal was to gain a deep understanding of the fundamental components that constitute modern language models and to implement them manually to solidify my knowledge.

What I've Learned

Tokenization Basics and Data Loaders

• Tokenization: Learned the fundamentals of breaking down text into tokens, handling vocabulary creation, and managing token indices.
• Data Loaders: Developed efficient data loaders for handling and preprocessing text datasets, ensuring smooth feeding of data into the model during training.

Bigram Language Model

• BigramLanguageModel: Implemented a simple bigram language model to understand the basics of probabilistic language modeling and how models predict the next word based on the previous one.

Self-Attention Transformers

• Self-Attention Mechanism: Explored how transformers use self-attention to weigh the importance of different tokens in a sequence, enabling the model to capture long-range dependencies.
• Transformer Architecture: Studied the overall architecture of transformers, including multi-head attention and feed-forward networks.

Residual Connections

• Residual Connections: Implemented residual (skip) connections to facilitate the training of deep networks by mitigating the vanishing gradient problem.

Batch Normalization and Dropout

• BatchNorm: Applied batch normalization to stabilize and accelerate the training process by normalizing layer inputs.
• Dropout: Utilized dropout techniques to prevent overfitting by randomly dropping neurons during training.

Current Progress

• Pretraining: Conducted extensive pretraining of a document completion transformer language model, achieving satisfactory performance in generating coherent and contextually relevant text completions.
• Model Architecture: Built and fine-tuned the transformer architecture with self-attention, residual connections, batch normalization, and dropout layers.

Next Steps

Enhancements and Implementations

1. Custom Tokenizer:

   • Develop a tokenizer from scratch to better understand the intricacies of tokenization and potentially improve tokenization performance tailored to specific datasets.

2. GPT-2 Implementation:

   • Implement the GPT-2 architecture to leverage its advanced capabilities in language modeling and generation.

Finetuning the Model

1. Gathering Data for Supervised Policy:

   • Collect and curate high-quality datasets to train a supervised policy, enhancing the model's ability to perform specific tasks.

2. Collecting Comparison Data:

   • Assemble comparison datasets to train the supervised policy, enabling the model to make informed decisions based on comparative analysis.

3. Optimizing Policy with Reward Models:

   • Utilize reward models such as Proximal Policy Optimization (PPO) reinforcement learning to fine-tune the policy, improving performance based on defined rewards.

Exploring Data Quality Optimization

• Investigate how the quality of training data impacts the performance of the GPT model.
• Implement strategies to enhance data quality, such as data cleaning, augmentation, and balancing, to optimize the model's effectiveness and reliability.

Future Directions

• Advanced Finetuning Techniques: Explore more sophisticated finetuning methods, including transfer learning and multi-task learning, to further enhance the model's versatility.
• Evaluation Metrics: Develop comprehensive evaluation metrics to assess model performance across various tasks and datasets.
• Deployment: Plan for deploying the trained model in real-world applications, ensuring scalability and robustness.

Conclusion

This project has provided a solid foundation in building and understanding language models from the ground up. By continuing to implement advanced features and focusing on finetuning and data optimization, I aim to develop a robust and high-performing language model capable of tackling complex natural language processing tasks.