o1 says:
Below is a self-contained, minimal example showing how you might implement and train a very simplified Byte Latent Transformer (BLT)-style model on a single machine with 1-2 GPUs. This is a toy implementation for experimentation and does not replicate the scale or exact performance of the official implementation. It focuses on the main architectural concepts: a local byte encoder, a global latent transformer operating on patches, and a local decoder. The code uses a simple fixed-stride patching scheme and trains on a dummy dataset of random bytes. You can adapt it to use your own dataset and incorporate entropy-based patching later.
What this example does:
