free-transformer 0.1.2

A Llama-style decoder architecture with explicit latent plans and conditional VAE training

Free Transformer

Free Transformer: A Llama-style decoder architecture with explicit latent plans, conditional VAE training, and benchmark comparisons against standard Transformers.

Designed for efficient PyTorch training on modern GPUs with full FSDP support and modern optimizations.

📖 Complete Documentation | 🚀 Quick Start Guide | 🏗️ Architecture Details

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What Is the Free Transformer?

Traditional autoregressive Transformers generate each token by conditioning only on the sequence so far ("reactive" behavior). Free Transformer introduces a latent planning mechanism—first choosing a stochastic abstract plan (Z), then generating tokens to fit that plan.
This scalable conditional VAE architecture maintains high-level coherence, improves controllable generation, and enables richer sequence modeling.

Architecture Overview

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Features

🏗️ Architecture

• Llama-style backbone: RMSNorm, SwiGLU, RoPE, Grouped-Query Attention (GQA)
• Latent Planning: Explicit plan variable Z with differentiable binary coding
• Conditional VAE: Reconstruction + KL loss with free bits regularization

⚡ Performance & Scaling

• FSDP Support: Multi-GPU training with PyTorch Fully Sharded Data Parallel
• Mixed Precision: Automatic Mixed Precision (AMP) with gradient scaling
• Memory Efficient: Gradient checkpointing and optimized attention patterns
• Modern Optimizations: bfloat16, efficient parameter sharding

🔧 Development & Training

• Flexible Training: Switchable inference/training flows with mode selection
• Synthetic + Real Data: Fast prototyping with built-in synthetic data generation
• Comprehensive Testing: Unit/integration tests, benchmark comparisons
• Quality Assurance: Type checking, linting, formatting, CI-ready

📦 Usability

• Extensible API: Modular classes, CLI scripts, YAML configuration
• Docker Support: Containerized demos and development environment
• Documentation: API references, architecture guides, examples

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Installation

From PyPI (Recommended)

pip install free-transformer

From Source

Using UV (recommended):

# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and install
git clone https://github.com/udapy/free-transformer.git
cd free-transformer
uv venv --python 3.12
source .venv/bin/activate
uv pip install -e ".[dev]"

Using pip:

git clone https://github.com/udapy/free-transformer.git
cd free-transformer
pip install -e ".[dev]"

📋 Detailed installation instructions: Installation Guide

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Quick Start

🐳 Docker (Fastest)

The fastest way to try Free Transformer:

git clone https://github.com/udapy/free-transformer.git
cd free-transformer
docker-compose up free-transformer-demo

🐍 Python API

from free_transformer import FreeTransformer, ModelConfig

# Create and train a model
config = ModelConfig(vocab_size=1000, hidden_dim=128, num_layers=6, latent_dim=8)
model = FreeTransformer(config)

# Training mode
import torch
tokens = torch.randint(0, 1000, (2, 128))
logits, z_logits = model(tokens, mode='training')

# Generation
generated = model.generate(tokens[:, :10], max_new_tokens=20)

🚀 Command Line

# Generate synthetic data and run demo
make demo

# Train models separately
make train-baseline  # Standard Transformer
make train-free      # Free Transformer
make compare         # Compare results

🎯 Complete tutorial: Quick Start Guide

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Manual Installation & Quick Start Demo

1. Generate Small Synthetic Data

   make generate-data-small
   

2. Train Baseline Transformer

   make train-baseline
   

3. Train Free Transformer

   make train-free
   

4. Run Model Comparison

   make compare
   

Or run the full pipeline:

make demo

Check results in:

• checkpoints/baseline/
• checkpoints/free/
• results/comparison/results.json

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Key Features Comparison

Feature	Standard Transformer	Free Transformer
Generation	Reactive (token-by-token)	Plan-then-generate
Coherence	Local	Global + Local
Controllability	Limited	High (via plan manipulation)
Training	Cross-entropy loss	Conditional VAE loss
Memory	Baseline	+10-15% (inference)
Speed	Baseline	-5-10% (inference)

🔬 Detailed comparison: Architecture Overview

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Repository Structure

free-transformer/
├── src/free_transformer/
│   ├── model.py
│   ├── baseline.py
│   ├── encoder.py
│   ├── latent.py
│   ├── injection.py
│   ├── losses.py
│   ├── synthetic_data.py
│   ├── train_utils.py
│   └── config.py
├── examples/
│   ├── train_baseline.py
│   ├── train_free.py
│   ├── eval_compare.py
│   └── generate_data.py
├── configs/
│   ├── baseline.yaml
│   └── free_transformer.yaml
├── docker/
│   ├── demo.sh
│   └── README.md
├── tests/
│   ├── unit/
│   ├── integration/
│   └── test_comparison.py
├── docs/
├── Dockerfile
├── Dockerfile.cpu
├── docker-compose.yml
├── Makefile
├── pyproject.toml
├── .python-version
├── LICENSE
└── README.md

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Testing & Quality

Run all tests:

make test

Quality checks:

make quality

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Advanced Features

🚀 Multi-GPU Training

# FSDP training with automatic GPU detection
make train-free-fsdp

# Custom distributed training
torchrun --nproc_per_node=auto examples/train_free.py --use-fsdp

📊 Flexible Data

• HuggingFace datasets integration
• Built-in synthetic data generation
• Custom data loading pipelines

🔧 Extensible Architecture

• Modular components for easy customization
• Custom loss functions and training schedules
• Plugin system for new features

📚 Learn more: Training Guide | Multi-GPU Setup

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Documentation

📖 Complete Documentation

Quick Links

• 🚀 Getting Started - Installation and setup
• 🏗️ Architecture - How Free Transformer works
• 🎯 Training Guide - Training best practices
• 📋 API Reference - Complete API documentation
• 💡 Examples - Code examples and tutorials
• ❓ FAQ - Frequently asked questions

Local Documentation

# Serve documentation locally
make docs-serve
# Open http://127.0.0.1:8000

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License

MIT License — see LICENSE

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Contributing

We welcome contributions! Please see our Contributing Guide for details.

Quick Development Setup

git clone https://github.com/udapy/free-transformer.git
cd free-transformer
make install-all  # Install with all dependencies
make test         # Run tests
make quality      # Check code quality

Before Submitting

• ✅ Tests pass: make test
• ✅ Code quality: make quality
• ✅ Documentation builds: make docs-build

📋 Full guidelines: Contributing Guide

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FAQ

Can I use this for real-world (non-synthetic) data?
Yes! Edit configs and use HuggingFace datasets.

How do I run distributed training?
Use provided CLI flags or edit config. See docs and Makefile.

How do I change architecture parameters?
Edit YAML config files for layer size, latent dim, number of blocks, etc.

Can I run this without installing dependencies locally?
Yes! Use Docker: docker-compose up free-transformer-demo for a complete demo.

What if I don't have a GPU?
Use the CPU Docker image: make docker-build-cpu && make docker-run-cpu

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Citation

If you use Free Transformer in your research, please cite:

@software{free_transformer,
  title={Free Transformer: Explicit Latent Planning for Autoregressive Generation},
  author={Phalak, Uday},
  year={2024},
  url={https://github.com/udapy/free-transformer},
  version={0.1.0}
}

Links

• 📦 PyPI Package
• 📖 Documentation
• 🐛 Issues
• 💬 Discussions

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Free Transformer - Bringing explicit planning to autoregressive generation

Documentation • PyPI • GitHub