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Features
Custom LLM Training
Provides a comprehensive guide to train a large language model (LLM) from scratch using PyTorch. It includes steps for dataset preparation, model architecture configuration, and training process.
Preprocessing Pipeline
Contains a detailed preprocessing procedure that tokenizes and organizes data into a suitable format for model training, ensuring efficient handling of large text corpora.
Model Configuration
Customizable model configuration options that allow users to define hyperparameters such as learning rate, batch size, and number of layers, tailoring the training process to specific needs.
Checkpointing System
A built-in checkpointing system that saves the model's state at defined intervals, allowing for recovery and fine-tuning without restarting the training from scratch.
Evaluation Metrics
Includes tools to evaluate the performance of the trained model using standard metrics like perplexity, helping in assessing language model quality.