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A discussion on future work exploring how byte-level models respond to quantization for potential improvements in local AI, based on a Meta paper showing byte models outperforming token models as compute scales.
This paper presents a large-scale study comparing distilled byte and token models, finding that byte models achieve higher performance ceilings with more compute and are more data-efficient than token models.