CoreML Speech Models
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Speech AI models for Apple Neural Engine via CoreML. iOS/macOS ready. ASR, TTS, VAD, diarization. ā¢ 23 items ā¢ Updated ā¢ 3
Menachem m Mann
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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!
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