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cs.LG2026
Attn-QAT: 4-Bit Attention With Quantization-Aware Training
Peiyuan Zhang, Matthew Noto, Wenxuan Tan +4
Achieving reliable 4-bit attention is a prerequisite for end-to-end FP4 computation on emerging FP4-capable GPUs, yet attention remains the main obstacle due to FP4's tiny dynamic…
cs.LG2024
ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization
Haoran You, Yipin Guo, Yichao Fu +6
Large language models (LLMs) have shown impressive performance on language tasks but face challenges when deployed on resource-constrained devices due to their extensive parameters…