pylmt 0.2.9

A PyTorch implementation of transformer-based language models including GPT architecture for pretraining and fine-tuning

Language Modeling using Transformers (LMT)

A PyTorch implementation of transformer-based language models including GPT architecture for pretraining and fine-tuning. This project is designed for educational and research purposes to help users understand how the attention mechanism and Transformer architecture work in Large Language Models (LLMs).

🚀 Features

• GPT Architecture: Complete implementation of decoder-only transformer models
• Attention Mechanisms: Multi-head self-attention with causal masking
• Tokenization: Multiple tokenizer implementations (BPE, Naive)
• Training Pipeline: Comprehensive trainer with pretraining and fine-tuning support
• Educational Focus: Well-documented code for learning transformer internals
• Modern Stack: Built with PyTorch 2.7+, Python 3.11+

📦 Installation

Prerequisites

• Python 3.11 or 3.12
• PyTorch 2.7+

Install from PyPI

pip install language-modeling-transformers

Install from GitHub

pip install git+https://github.com/michaelellis003/LMT.git

🏃‍♂️ Quick Start

Basic Model Usage

from lmt import GPT, ModelConfig
from lmt.models.config import ModelConfigPresets
import torch

# Create a small GPT model
config = ModelConfigPresets.small_gpt()
model = GPT(config)

# Generate some text
input_ids = torch.randint(0, config.vocab_size, (1, 10))
with torch.no_grad():
    logits = model(input_ids)
    print(f"Output shape: {logits.shape}")  # (1, 10, vocab_size)

Training a Model

from lmt import Trainer, GPT
from lmt.training import BaseTrainingConfig
from lmt.models.config import ModelConfigPresets

# Configure model and training
model_config = ModelConfigPresets.small_gpt()
training_config = BaseTrainingConfig(
    num_epochs=10,
    batch_size=4,
    learning_rate=1e-4
)

# Initialize model and trainer
model = GPT(model_config)
trainer = Trainer(
    model=model,
    train_loader=your_train_loader,
    val_loader=your_val_loader,
    config=training_config
)

# Start training
trainer.train()

Using the Training Script

# Pretraining
python scripts/train.py --task pretraining --num_epochs 20 --batch_size 4

# Classification fine-tuning
python scripts/train.py --task classification --download_model --learning_rate 1e-5

📚 Documentation

Model Components

• GPT: Main model class implementing decoder-only transformer
• TransformerBlock: Individual transformer layer with attention and feed-forward
• MultiHeadAttention: Multi-head self-attention mechanism
• CausalAttention: Attention with causal masking for autoregressive generation

Tokenizers

• BPETokenizer: Byte-Pair Encoding tokenizer
• NaiveTokenizer: Simple character-level tokenizer
• BaseTokenizer: Abstract base class for custom tokenizers

Training

• Trainer: Main training orchestrator with support for pretraining and fine-tuning
• BaseTrainingConfig: Configuration class for training parameters
• Custom datasets and dataloaders: Support for various text datasets

🗂️ Project Structure

src/lmt/
├── __init__.py              # Main package exports
├── models/                  # Model architectures
│   ├── gpt/                # GPT implementation
│   ├── config.py           # Model configuration
│   └── utils.py            # Model utilities
├── layers/                  # Neural network layers
│   ├── attention/          # Attention mechanisms
│   └── transformers/       # Transformer blocks
├── tokenizer/              # Tokenization implementations
├── training/               # Training pipeline
└── generate.py             # Text generation utilities

scripts/
├── train.py                # Main training script
└── utils.py                # Training utilities

tests/                      # Comprehensive test suite
notebooks/                  # Educational Jupyter notebooks
docs/                       # Sphinx documentation

📊 Examples and Notebooks

Explore the interactive notebooks in the notebooks/ directory:

• attention.ipynb: Understanding attention mechanisms
• pretraining_gpt.ipynb: GPT pretraining walkthrough
• tokenizer.ipynb: Tokenization techniques

🔧 Configuration

Model Configuration

from lmt.models.config import ModelConfig

config = ModelConfig(
    vocab_size=50257,
    embed_dim=768,
    context_length=1024,
    num_layers=12,
    num_heads=12,
    dropout=0.1
)

Training Configuration

from lmt.training.config import BaseTrainingConfig

training_config = BaseTrainingConfig(
    num_epochs=10,
    batch_size=8,
    learning_rate=3e-4,
    weight_decay=0.1,
    print_every=100,
    eval_every=500
)

📄 License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.