Cambrian Stack

Minimal, hackable transformer experiments. For full docs, see https:// (built from docs/ via GitHub Pages).

Quick start

git clone <repo-url> && cd cambrian-stack
uv venv && source .venv/bin/activate
uv pip install -e ".[ml]"   # or ".[dev]" for tooling; base install skips torch

./scripts/train_baseline_transformer.sh                # baseline AR
python -m cambrian_stack.train --config-name=diffusion_transformer  # diffusion

Outputs: checkpoints in out/, logs in logs/, optional W&B if WANDB_API_KEY is set.

Configs

Hydra configs live in src/cambrian_stack/conf/ (grouped by experiment/model/data/training/logging/output). Example overrides:

python -m cambrian_stack.train training.max_steps=2000 training.eval_every=200
python -m cambrian_stack.train --config-name=baseline_transformer model.depth=6

Experiments

• Autoregressive (GPT-style) and diffusion strategies are registered in cambrian_stack/experiments. Add new ideas by adding an experiment module + config, without touching the trainer.

Docs

• Build locally: pip install -e ".[docs]" && sphinx-build -b html docs docs/_build/html
• Deploy: GitHub Actions workflow docs.yml publishes to Pages.

License

MIT