madhumithakolkar / mk_llm

Bigram Character-Level Language Model

This repository contains the code for a bigram word-level language model implemented using a Transformer architecture. The model is designed to predict the next word in a sequence based on the previous word (bigram).

Requirements:

Python 3.x PyTorch mmap argparse Instructions:

Clone this repository.

Install the required dependencies:

Bash pip install torch mmap argparse

Prepare your data:

Create vocabulary files for words (one file per split: train, validation). Each line should contain a unique word from your training data. Split your text data into training and validation sets (e.g., using a script or manually). Save them as plain text files. Run the training script:

Bash python train.py -batch_size 32 # Adjust batch size as needed Use code with caution. content_copy This script assumes your vocabulary files are named vocab_words.txt and your split files are named train_split.txt and val_split.txt. You can modify the script to use different file names.

(Optional) Evaluate the trained model on the validation set. You can modify the train.py script to add evaluation functionality.

Project Structure:

train.py: Script for training the bigram language model. vocab_words.txt: (Replace with your actual vocabulary file name) Vocabulary file containing unique words (one per line). train_split.txt: (Replace with your actual file name) Text data split for training. val_split.txt: (Replace with your actual file name) Text data split for validation. Further Considerations:

This is a basic implementation for demonstration purposes. You can explore advanced techniques for improving performance, such as: Subword tokenization (e.g., Byte Pair Encoding) for handling unknown words. Pre-trained word embeddings (e.g., Word2Vec, GloVe) for richer word representations. Optimization techniques like gradient accumulation or mixed precision training for memory efficiency. Experiment with different hyperparameters (e.g., vocabulary size, embedding size, batch size, learning rate) to find the best configuration for your dataset.