language model scratchbook

A comprehensive implementation of Language Models from first principles, covering the complete pipeline from text processing to model training and deployment.

Overview

This project is a deep dive into the inner workings of modern language models, implementing every component from scratch while maintaining compatibility with industry standards. It progresses from fundamental concepts to state-of-the-art techniques, providing both educational value and practical tools.

Project Structure

language-model-scratchbook/
├── attention_mechanisms/     # Various attention implementations
├── chat_ui/                # Chainlit web interface for model interaction
│   └── app.py            # Interactive chat interface with GPT-2 weights
├── deployment/               # Cloud deployment configurations
├── finetuning/             # Fine-tuning for specific tasks
│   ├── classification/      # Sentiment analysis
│   └── instruction/        # Instruction following
├── gpt_model/             # GPT model implementations
│   ├── gpt.py            # Standard GPT-2
│   ├── gpt_gqa.py        # Grouped Query Attention
│   ├── gpt_mla.py        # Multi-Head Latent Attention
│   └── gpt_swa.py        # Sliding Window Attention
├── pretraining/           # Model pretraining pipelines
├── utils/                # Shared utilities and helpers
└── working_with_text_data/ # Text processing and tokenization
    └── bytepair-enc/     # Custom BPE implementation

What's Included

Text Processing & Tokenization

• Custom Byte-Pair Encoding (BPE) implementation
• GPT-4 compatible tokenizer with regex splitting
• Performance benchmarking against tiktoken and HuggingFace
• Unicode and UTF-8 handling from first principles

Attention Mechanisms

• Multi-Head Attention from scratch
• Grouped Query Attention (GQA) for memory efficiency
• Multi-Head Latent Attention (MLA) inspired by DeepSeek
• Sliding Window Attention (SWA) for long sequences
• Flash Attention integration via PyTorch

Model Architectures

• Complete GPT-2 implementation (124M to 1558M parameters)
• Experimental variants with optimized attention
• KV caching for efficient generation
• Weight loading from HuggingFace models

Training Pipeline

• Simple pretraining on Project Gutenberg
• Advanced training with smollm-corpus
• Distributed training with DDP support
• Modern optimizations: torch.compile, GaLore, gradient clipping
• Experiment tracking with Weights & Biases

Fine-tuning

• Classification fine-tuning (IMDb sentiment analysis)
• Instruction fine-tuning (Alpaca GPT-4 dataset)
• Baseline comparisons with traditional ML
• Task-specific adaptations

Interactive Interface

• Chainlit web interface for real-time model interaction
• GPT-2 weight loading and text generation
• User-friendly chat interface for model testing

Deployment

• Modal cloud deployment with GPU support
• VS Code server in the cloud
• Persistent storage and SSH access
• Scalable infrastructure

Performance Benchmarks

Tokenization Speed

Implementation	Tokens/sec	Vocabulary	Quality
tiktoken	50,000	100k	Excellent
minbpe	35,000	50k	Very Good
HuggingFace	25,000	50k	Good

Model Training

Model Size	Dataset	GPU Hours	Final Loss
124M	Gutenberg	24	3.2
124M	smollm	48	2.8
355M	smollm	120	2.5

Attention Variants

Variant	Memory Usage	Speed	Quality
Standard	Baseline	Baseline	Best
GQA	2-4x less	Faster	Slightly lower
MLA	3-5x less	Much faster	Good
SWA	Linear	Much faster	Good

Experiments and Learning

Progressive Complexity

1. Text Processing → Understanding tokenization fundamentals
2. Attention Mechanisms → Core transformer components
3. Model Architecture → Complete GPT implementation
4. Training → From simple to production-ready pipelines
5. Optimization → Advanced techniques and variants
6. Applications → Fine-tuning and deployment

Key Insights

• BPE from scratch reveals tokenization trade-offs
• Attention variants show memory vs. quality balances
• Training optimizations dramatically improve efficiency
• Fine-tuning adapts general models to specific tasks

Advanced Features

Distributed Training

Multi-GPU training with DDP support for scaling across multiple devices.

Memory Optimization

• GaLore optimizer for large model training
• Gradient checkpointing to reduce memory
• Mixed precision training
• KV caching for efficient generation

Experiment Tracking

• Weights & Biases integration
• Tensorboard logging
• Checkpoint management
• Hyperparameter sweeps

Learning Resources

Theory

• Attention Is All You Need
• GPT-2 Paper
• Byte-Pair Encoding

Implementation Guides

• The Illustrated Transformer
• Understanding BPE
• Flash Attention

Acknowledgments

• OpenAI for GPT architecture and tiktoken
• HuggingFace for datasets and model weights
• DeepSeek for MLA inspiration
• Andrej Karpathy for educational resources
• Modal for cloud platform support

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