Exciting breakthrough in AI: 
text
Exciting breakthrough in AI: 
@Meta
mention
Meta
's new Byte Latent Transformer (BLT) revolutionizes language models by eliminating tokenization!
text
's new Byte Latent Transformer (BLT) revolutionizes language models by eliminating tokenization!
new_line
new_line
The BLT architecture introduces a groundbreaking approach that processes raw bytes instead of tokens, achieving state-of-the-art performance while being more efficient and robust. Here's what makes it special:
text
The BLT architecture introduces a groundbreaking approach that processes raw bytes instead of tokens, achieving state-of-the-art performance while being more efficient and robust. Here's what makes it special:
new_line
new_line
>> Key Innovations
text
>> Key Innovations
new_line
Dynamic Patching: BLT groups bytes into variable-sized patches based on entropy, allocating more compute power where the data is more complex. This results in up to 50% fewer FLOPs during inference compared to traditional token-based models.
text
Dynamic Patching: BLT groups bytes into variable-sized patches based on entropy, allocating more compute power where the data is more complex. This results in up to 50% fewer FLOPs during inference compared to traditional token-based models.
new_line
new_line
Three-Component Architecture:
text
Three-Component Architecture:
new_line
• Lightweight Local Encoder that converts bytes to patch representations
text
• Lightweight Local Encoder that converts bytes to patch representations
new_line
• Powerful Global Latent Transformer that processes patches
text
• Powerful Global Latent Transformer that processes patches
new_line
• Local Decoder that converts patches back to bytes
text
• Local Decoder that converts patches back to bytes
new_line
new_line
>> Technical Advantages
text
>> Technical Advantages
new_line
• Matches performance of Llama 3 at 8B parameters while being more efficient
text
• Matches performance of Llama 3 at 8B parameters while being more efficient
new_line
• Superior handling of non-English languages and rare character sequences
text
• Superior handling of non-English languages and rare character sequences
new_line
• Remarkable 99.9% accuracy on spelling tasks
text
• Remarkable 99.9% accuracy on spelling tasks
new_line
• Better scaling properties than token-based models
text
• Better scaling properties than token-based models
new_line
new_line
>> Under the Hood
text
>> Under the Hood
new_line
The system uses an entropy model to determine patch boundaries, cross-attention mechanisms for information flow, and hash n-gram embeddings for improved representation. The architecture allows simultaneous scaling of both patch and model size while maintaining fixed inference costs.
text
The system uses an entropy model to determine patch boundaries, cross-attention mechanisms for information flow, and hash n-gram embeddings for improved representation. The architecture allows simultaneous scaling of both patch and model size while maintaining fixed inference costs.
new_line
new_line
This is a game-changer for multilingual AI and could reshape how we build future language models. Excited to see how this technology evolves!
text
This is a game-changer for multilingual AI and could reshape how we build future language models. Excited to see how this technology evolves!
Exciting breakthrough in AI: @Meta's new Byte Latent Transformer (BLT) revolutionizes language models by eliminating tokenization!

The BLT architecture introduces a groundbreaking approach that processes raw bytes instead of tokens, achieving state-of-the-art performance while being more efficient and robust. Here's what makes it special:

>> Key Innovations
Dynamic Patching: BLT groups bytes into variable-sized patches based on entropy, allocating more compute power where the data is more complex. This results in up to 50% fewer FLOPs during inference compared to traditional token-based models.

Three-Component Architecture:
• Lightweight Local Encoder that converts bytes to patch representations
• Powerful Global Latent Transformer that processes patches
• Local Decoder that converts patches back to bytes

>> Technical Advantages
• Matches performance of Llama 3 at 8B parameters while being more efficient
• Superior handling of non-English languages and rare character sequences
• Remarkable 99.9% accuracy on spelling tasks
• Better scaling properties than token-based models

>> Under the Hood
The system uses an entropy model to determine patch boundaries, cross-attention mechanisms for information flow, and hash n-gram embeddings for improved representation. The architecture allows simultaneous scaling of both patch and model size while maintaining fixed inference costs.

This is a game-changer for multilingual AI and could reshape how we build future language models. Excited to see how this technology evolves!
🧠
GoDjMike John6666 cabralski Mmann andyblackard JbJaz 50mf Joseph717171
🚀
whitebill etemiz Joseph717171 MexIvanov johnlockejrr
65a488b5224f96d8cc3754fc
/avatars/cf21cf2c8f1c9d5a8fb35761acdef04b.svg
Emin Temiz
etemiz
user
6538c682ad8f7c2cbe896108
/avatars/a39a64c15ea487decfdff8990ddd3415.svg
wenyang
notoookay
user
61e8c67cee1e1440121f0240
https://cdn-avatars.huggingface.co/v1/production/uploads/61e8c67cee1e1440121f0240/7akLZSb6xJZzbnl0o9Cod.jpeg
Jonas
Meta
user