TinyWorlds is a minimal autoregressive world model built on Google Deepmind's Genie Architecture.
World models can't use action-less internet video to scale like VEO3. Deepmind's Genie solves this by inferring the actions between frames using no prior action data.
TinyWorlds is meant to help people understand the clever autoregressive, unsupervised method Deepmind likely used to achieve scalable world models.
Table of Contents
Getting Started
# Installation git clone https://github.com/AlmondGod/tinyworlds.git cd tinyworlds pip install -r requirements.txt export WANDB_API_KEY= < YOUR_WANDB_API_KEY > export PYTHONPATH= " /workspace/tinyworlds: $PYTHONPATH " # Training # 1. download data from huggingface python scripts/download_assets.py datasets --pattern " zelda_frames.h5 " # 2. run training python scripts/full_train.py --config configs/training.yaml -- --dataset=ZELDA # Inference # 1. pull pretrained sonic checkpoints from huggingface python scripts/download_assets.py models --suite-name sonic # 2. run inference python scripts/run_inference.py --config configs/inference.yaml -- use_latest_checkpoints=true dataset=SONIC
Overview
Why World Models?
How do we bend reality to our will? we generate reality itself…
A world model is simply a function mapping the current state of the environment to the next state of the environment.
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