Medusa augments LLMs with multiple decoding heads and tree-based attention to predict and verify several tokens in parallel, yielding 2.2-3.6x inference speedup via two fine-tuning regimes.
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SynPro uses RL-optimized rephrasing and reformatting of organic data to generate synthetic pretraining tokens that deliver 3.7-5.2x the effective learning of simple repetition and can exceed training on unique data at 1.1B scale.
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Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
Medusa augments LLMs with multiple decoding heads and tree-based attention to predict and verify several tokens in parallel, yielding 2.2-3.6x inference speedup via two fine-tuning regimes.
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Generating Pretraining Tokens from Organic Data for Data-Bound Scaling
SynPro uses RL-optimized rephrasing and reformatting of organic data to generate synthetic pretraining tokens that deliver 3.7-5.2x the effective learning of simple repetition and can exceed training on unique data at 1.1B scale.