𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺 𝘀𝗵𝗶𝗳𝘁 𝗶𝗻 𝗟𝗟𝗠𝘀: 𝗻𝗲𝘄 𝗽𝗮𝗽𝗲𝗿 𝗯𝘆 𝗠𝗲𝘁𝗮 𝗰𝗹𝗮𝗶𝗺𝘀 𝘁𝗵𝗮𝘁 𝘄𝗲 𝗰𝗮𝗻 𝗴𝗲𝘁 𝗿𝗶𝗱 𝗼𝗳 𝘁𝗼𝗸𝗲𝗻𝗶𝘇𝗲𝗿𝘀! 🥳

Current LLMs process text by first splitting it into tokens. They use a module named "tokenizer", that -spl-it-s- th-e- te-xt- in-to- arbitrary tokens depending on a fixed dictionnary.
On the Hub you can find this dictionary in a model's files under tokenizer.json.

➡️ This process is called BPE tokenization. It is suboptimal, everyone says it. It breaks text into predefined chunks that often fail to capture the nuance of language. But it has been a necessary evil in language models since their inception.

💥 In Byte Latent Transformer (BLT), Meta researchers propose an elegant solution by eliminating tokenization entirely, working directly with raw bytes while maintaining efficiency through dynamic "patches."

This had been tried before with different byte-level tokenizations, but it's the first time that an architecture of this type scales as well as BPE tokenization. And it could mean a real paradigm shift! 👏👏

🏗️ 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲:
Instead of a lightweight tokenizer, BLT has a lightweight encoder that process raw bytes into patches. Then the patches are processed by the main heavy-duty transformers as we do normally (but for patches of bytes instead of tokens), before converting back to bytes.

🧩 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗮𝘁𝗰𝗵𝗶𝗻𝗴:
Instead of fixed tokens, BLT groups bytes based on their predictability (measured by entropy) - using more compute for complex sequences and efficiently handling simple ones. This allows efficient processing while maintaining byte-level understanding.

I hope this breakthrough is confirmed and we can get rid of all the tokenizer stuff, it will make model handling easier!

Read their paper here 👉 https://dl.fbaipublicfiles.com/blt/BLT__Patches_Scale_Better_Than_Tokens.pdf