Hey folks, it’s your byte-crunching buddy alBz here. No pizza, no mandolino today, we’re diving into something even spicier: Meta AI’s Byte Latent Transformer, or BLT for short. And no, it’s not a sandwich... but it might just be the tastiest thing in AI right now.
What’s the Big Idea
Forget everything you know about tokenization (except git commit messages, those still need tokens). The paper Byte Latent Transformer: Patches Scale Better Than Tokens proposes a large language model that completely ditches tokenization and works directly with raw bytes.
Yes. Bytes. Raw. Uncut.
Like a Quentin Tarantino film for AI models.
How Does It Work
BLT uses a patch-based system that’s more dynamic than your weekend plans.
Local Encoder: Chops raw bytes into smart, variable-length patches.
Think of it like slicing a pizza based on how hungry you are.Latent Transformer: Processes these patches using transformer magic.
Local Decoder: Reconstructs the output from processed patches.
Kind of like reassembling IKEA furniture, but this time, it actually works.
The key is entropy-based segmentation:
More unpredictability? Smaller patches.
Boring input? Larger patches.
In short: it spends compute where it matters. Like a good engineer ignoring meetings to fix production.
Why Should You Care
- No Tokenizer Needed
BLT skips vocabulary headaches and handles multilingual inputs, emojis, and all your wild characters like a ninja.
- Crazy Efficient
By adjusting patch sizes on the fly, BLT makes training and inference way more efficient.
- Robust as Hell
It handles noisy input and weird edge cases better than most of us handle Mondays.
- Scales Like a Beast
Trained up to 8 billion parameters on 4 trillion bytes, and still outperforms traditional token-based LLMs like LLaMA 2 and 3 at the same compute budget.
Can I Try It
Of course you can.
GitHub Repo: facebookresearch/blt
Final Thoughts
This model is a glimpse into the post-tokenization future, one byte at a time. It won’t replace all token-based LLMs overnight, but it’s a bold reminder of what becomes possible when you shift your position, and your perspective along with it.
…especially if you’ve got a few thousand H100s and a couple hundred million dollars lying around, like Meta did while training LLaMA.