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H-Nets Learn Directly From Raw Bytes
Cartesia's H-Nets avoid the tokenization bottleneck by processing raw bytes directly, achieving state-of-the-art performance with significantly lower training compute.
What: Cartesia's H-Nets (Hierarchical Networks) bypass traditional tokenization to learn representations directly from raw data bytes. This architecture matches benchmark performance while drastically reducing the computational requirements typical of current transformer-based models.
Why it matters: Moving away from tokenization could eliminate the inefficiencies and biases introduced by byte-pair encoding, potentially enabling models that better understand sub-word structures and raw file formats.
Decoder
- Tokenization: The process of breaking down text into smaller units (tokens) before feeding it into a neural network, which often causes information loss or complexity in rare languages/formats.
- Compute: The total amount of processing power (measured in floating-point operations) required to train or run an AI model.
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