Weight gradient FP4 quantization drives LLM pretraining divergence, which deterministic Hadamard rotations can stabilize on native MXFP4 hardware.
Advances in Neural Information Processing Systems , volume=
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LBLLM achieves better accuracy than prior binarization methods for LLMs by decoupling weight and activation quantization through initialization, layer-wise distillation, and learnable activation scaling.
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Pretraining large language models with MXFP4 on Native FP4 Hardware
Weight gradient FP4 quantization drives LLM pretraining divergence, which deterministic Hadamard rotations can stabilize on native MXFP4 hardware.
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LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
LBLLM achieves better accuracy than prior binarization methods for LLMs by decoupling weight and activation quantization through initialization, layer-wise distillation, and learnable activation scaling.