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Quantizing Large LLMs Without Performance Drop

How do you quantize LLMs with tens of billions of parameters while avoiding the sharp drop in performance seen when quantizing models > 6B params? Listen to @aahmadian_ highlight a correct training optimization recipe (which leads to no drop in performance when quantizing vs. a

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