Meta's new BLT architecture processes raw bytes dynamically, offering a novel approach that enables better adaptability to varied text formats and languages.
This dynamic approach leads to a significant reduction in inference flops while matching the performance of advanced token-based models like Llama 3.
BLT excels in handling edge cases such as correcting misspellings and working with noisy text, offering superior performance compared to token-based models.
The design of BLT allows for scaling language models more efficiently, allowing simultaneous growth in model size and the average size of byte groups.
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