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Stefano Fazzino

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#small language model Open access Oct 2026

How Many Bits Does a Fact Need? Memory Load, Competing Alternatives and Weight Quantization in a Small Transformer

This work investigates how post-training weight quantization affects the knowledge memorized by a language model. To this end, we train a transformer with 1.1 million parameters on synthetic biographies, a setting in which the amount of information is known exactly, and we vary both the information load and the number...

Stefano Fazzino · 0 citations
#small language model Open access Oct 2026

How Many Bits Does a Fact Need? Memory Load, Competing Alternatives and Weight Quantization in a Small Transformer

This work investigates how post-training weight quantization affects the knowledge memorized by a language model. To this end, we train a transformer with 1.1 million parameters on synthetic biographies, a setting in which the amount of information is known exactly, and we vary both the information load and the number...

Stefano Fazzino · 0 citations

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