xororz/sdxl-qnn
Updated ā¢ 20
Menachem m Mann
Mmann
AI & ML interests
None yet
Recent Activity
liked a model 14 days ago
xororz/sdxl-qnn upvoted a collection 28 days ago
DictaBERT upvoted a collection about 1 month ago
CoreML Speech ModelsOrganizations
None yet
liked a
model 14 days ago
upvoted a collection 28 days ago
upvoted a collection about 1 month ago
liked a
model about 1 month ago
liked a
dataset 2 months ago
liked a
model 4 months ago
liked a
model 10 months ago
liked a
Space over 1 year ago
liked a
dataset over 1 year ago
reacted to singhsidhukuldeep's post with š§ over 1 year ago
Post
3752
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:
ā¢ Lightweight Local Encoder that converts bytes to patch representations
ā¢ Powerful Global Latent Transformer that processes patches
ā¢ Local Decoder that converts patches back to bytes
>> Technical Advantages
ā¢ Matches performance of Llama 3 at 8B parameters while being more efficient
ā¢ Superior handling of non-English languages and rare character sequences
ā¢ Remarkable 99.9% accuracy on spelling tasks
ā¢ 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!
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:
ā¢ Lightweight Local Encoder that converts bytes to patch representations
ā¢ Powerful Global Latent Transformer that processes patches
ā¢ Local Decoder that converts patches back to bytes
>> Technical Advantages
ā¢ Matches performance of Llama 3 at 8B parameters while being more efficient
ā¢ Superior handling of non-English languages and rare character sequences
ā¢ Remarkable 99.9% accuracy on spelling tasks
ā¢ 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!
- 3 replies
liked a
Space over 1 year ago
upvoted a collection almost 2 years ago
liked a
model almost 2 years ago
liked a
model over 2 years ago
liked 3
Spaces over 2 years ago
liked a
model over 2 years ago
liked 2
Spaces almost 3 years ago