Hidden layer distillation yields systematic perplexity gains over logit KD in LLM pre-training but does not consistently improve downstream performance.
5th International Conference on Learning Representations
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Empirical evaluation of quantization effects on eight LLMs across bit widths, showing performance generally declines at lower precision but with model-size-dependent resilience and acceptable accuracy at 2 bits for many cases.
citing papers explorer
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A Study on Hidden Layer Distillation for Large Language Model Pre-Training
Hidden layer distillation yields systematic perplexity gains over logit KD in LLM pre-training but does not consistently improve downstream performance.
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K-Quantization and its Impact on Output Performance
Empirical evaluation of quantization effects on eight LLMs across bit widths, showing performance generally declines at lower precision but with model-size-dependent resilience and acceptable accuracy at 2 bits for many cases.
- The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs