Your code implements a character-level language model inspired by nanoGPT, designed to predict and generate text at the character level. Here's an overview of its key components and functionalities:
Model Architecture:
Embedding Layers:The model employs layers to convert input characters into continuous vector representations, capturing semantic nuances.
Positional Encoding:A positional embedding is added to incorporate the order of characters, enabling the model to understand sequence structure.
Transformer Blocks:The architecture includes multiple layers of transformer blocks, each comprising:
- Multi-Head Self-Attention:Allows the model to focus on different parts of the sequence simultaneously, capturing contextual relationships.
- Feed-Forward Neural Network:Processes the output of the attention mechanism, introducing non-linear transformations.
- Layer Normalization:Ensures stable and efficient training by normalizing inputs across the features.
Output Layer:A linear layer projects the processed embeddings back to the vocabulary size, producing logits for the next character prediction.
Training Process:
Data Preparation:The text data is read and split into training and validation sets. Each character is mapped to a unique integer, creating input sequences and their corresponding targets.
Batch Generation:Generates batches of input-target pairs for training, facilitating efficient learning.
Loss Estimation:Evaluates the model's performance on both training and validation sets, guiding hyperparameter tuning and model adjustments.
Optimization:Utilizes the AdamW optimizer to minimize the cross-entropy loss between the predicted logits and actual targets, updating model weights accordingly.
Text Generation:
Sampling Mechanism: Starting from a given context, the model predicts subsequent characters iteratively. At each step, it:
- Feeds the current sequence to the model to obtain logits for the next character.
- Applies softmax to convert logits into probabilities.
- Samples from the probability distribution to select the next character.
- Appends the chosen character to the context and repeats the process for the desired length.
Key Features: Character-Level Modeling:Captures fine-grained details of text, making it versatile for various languages and specialized vocabularies.
Scalability:The model's design allows training on machines with limited computational resources, such as personal laptops or single GPUs.
Simplicity and Readability:The code is structured to be straightforward and easy to understand, facilitating experimentation and customization.
This implementation serves as a foundational framework for character-level language modeling, offering flexibility for further enhancements and adaptations to specific tasks or datasets.