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SFT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
Xinyu Yang, Jixuan Leng, Geyang Guo +5
Current PEFT methods for LLMs can achieve either high quality, efficient training, or scalable serving, but not all three simultaneously. To address this limitation, we investigate…
VcLLM: Video Codecs are Secretly Tensor Codecs
Ceyu Xu, Yongji Wu, Xinyu Yang +4
As the parameter size of large language models (LLMs) continues to expand, the need for a large memory footprint and high communication bandwidth have become significant bottleneck…
It Takes Two: On the Seamlessness between Reward and Policy Model in RLHF
Taiming Lu, Lingfeng Shen, Xinyu Yang +3
Reinforcement Learning from Human Feedback (RLHF) involves training policy models (PMs) and reward models (RMs) to align language models with human preferences. Instead of focusing…
Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity
Wentao Guo, Jikai Long, Yimeng Zeng +9
Zeroth-order optimization (ZO) is a memory-efficient strategy for fine-tuning Large Language Models using only forward passes. However, the application of ZO fine-tuning in memory-…
TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding
Hanshi Sun, Zhuoming Chen, Xinyu Yang +2
With large language models (LLMs) widely deployed in long content generation recently, there has emerged an increasing demand for efficient long-sequence inference support. However…
Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference
Harry Dong, Xinyu Yang, Zhenyu Zhang +3
Many computational factors limit broader deployment of large language models. In this paper, we focus on a memory bottleneck imposed by the key-value (KV) cache, a computational sh…