tinyGPT

In this project, I've developed a sophisticated language model based on the Generative Pre-trained Transformer (GPT) architecture using PyTorch. The model is designed to generate coherent and contextually relevant text based on a given input sequence. Here's a detailed summary of my work:

1. Data Preprocessing:

   • The text data was preprocessed at the character level, where each character was encoded as a unique integer. This encoding facilitated direct processing of raw text data by the model.
   • I divided the dataset into training and validation sets to enable model evaluation and prevent overfitting.

2. Model Architecture:

   • My model architecture followed the Transformer framework, a powerful architecture known for its effectiveness in sequence modeling tasks.
   • It consisted of embedding layers, including token and positional embeddings, which captured semantic and positional information of tokens in the input sequence, respectively.
   • Transformer blocks formed the core of the model, incorporating self-attention mechanisms, feedforward neural networks, and layer normalization. These blocks enabled the model to capture relationships between tokens efficiently and learn complex patterns in the data.
   • A language model head, comprising a linear layer followed by a softmax function, was employed to predict the probability distribution over the vocabulary for the next token, facilitating text generation.

3. Training:

   • During training, the model predicted the next token in the sequence, and the cross-entropy loss was computed between the predicted logits and the actual targets.
   • I used the AdamW optimizer to update the model parameters, employing weight decay regularization to prevent overfitting and improve generalization.

4. Text Generation:

   • Given a context sequence, the trained model generated text by probabilistically predicting the next token based on its learned representations.
   • I performed sampling iteratively until the desired length of text was achieved, resulting in human-like text resembling the input data.

5. Advanced Techniques Utilized:

   • My model integrated advanced techniques such as Transformers, self-attention mechanisms, positional encoding, layer normalization, cross-entropy loss, and the AdamW optimizer.
   • These techniques collectively enabled the model to capture long-range dependencies, differentiate between tokens based on their positions, stabilize training, and optimize parameter updates efficiently.

In summary, my language model exemplifies a deep understanding of state-of-the-art natural language processing techniques and architectures. It demonstrates my proficiency in implementing complex models to generate high-quality text output, showcasing my expertise in the field of deep learning and natural language processing.