4 papers
Is Human-Readable Text Necessary for Effective LLM Fine-Tuning?
Jinhao Zhang, Zeyu Liu, Zicheng Yan +3
Is human readability necessary for effective fine-tuning of large language models? We investigate whether model-conditioned training representations can preserve or improve adaptat…
Mechanisms of Width Scaling in Normalized Residual Networks: The Effective Alignment Dimension
Jinhao Zhang, Zeyu Liu, Zicheng Yan +4
Existing theories of neural-network width characterize asymptotic limits, but provide limited guidance on whether an expansion direction identified from finite training data remain…
CALM: A CKA-Guided Adaptive Layer-Wise Modularization Framework for LLM Quantization
Jinhao Zhang, Yunquan Zhang, Daning Chen +2
Current mainstream post-training quantization methods for large language models typically apply a uniform quantization strategy across all network layers, overlooking the substanti…
HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning
Jinhao Zhang, Yunquan Zhang, Zicheng yan +3
Post Training Quantization (PTQ), a mainstream model compression technique, often leads to the paradoxical 'low error, high loss' phenomenon because it focuses solely on minimizing…