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The roadmap for small models is here! Apple has clarified the "Distillation Scaling Law".
Research on Apple has found that multiple "distillations" are more advantageous, and the performance of the "teacher" model is more important than its size. A more powerful "teacher" (large model) can sometimes produce a weaker "student" (small model), and when the "capability gap" between the two is too large, it is actually detrimental to distillation. In other words, a suitable teacher is needed for effective learning to occur.
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