Article

Qft: Quantized full-parameter tuning of llms with affordable resources
Large Language Models (LLMs) have showcased remarkable impacts across a wide spectrum of natural language processing tasks. Fine-tuning …
引用 DOI
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation
Model quantization is a promising method for accelerating and compressing diffusion models. Nevertheless, since post-training …
引用 代码 DOI
LLM Inference Unveiled: Survey and Roofline Model Insights
The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and …
引用 代码 DOI
RepQuant: Towards Accurate Post-Training Quantization of Large Transformer Models via Scale Reparameterization
Large transformer models have demonstrated remarkable success. Post-training quantization (PTQ), which requires only a small dataset …
引用 DOI
EDA-DM: Enhanced distribution alignment for post-training quantization of diffusion models
Diffusion models have achieved great success in image generation tasks through iterative noise estimation. However, the heavy denoising …
引用 DOI
BinaryViT: Towards Efficient and Accurate Binary Vision Transformers
Vision Transformers (ViTs) have emerged as the fundamental architecture for most computer vision fields, but the considerable memory …
引用 DOI