6 papers · 1 filter
ZigzagAttention: Efficient Long-Context Inference with Exclusive Retrieval and Streaming Heads
Zhuorui Liu, Chen Zhang, Dawei Song
With the rapid development of large language models (LLMs), handling long context has become one of the vital abilities in LLMs. Such long-context ability is accompanied by difficu…
Towards the Law of Capacity Gap in Distilling Language Models
Chen Zhang, Qiuchi Li, Dawei Song +3
Language model (LM) distillation aims at distilling the knowledge in a large teacher LM to a small student one. As a critical issue facing LM distillation, a superior student often…
WindowKV: Task-Adaptive Group-Wise KV Cache Window Selection for Efficient LLM Inference
Youhui Zuo, Sibo Wei, Chen Zhang +3
With the advancements in long-context inference capabilities of large language models (LLMs), the KV cache has become one of the foundational components. However, its substantial G…
MoDification: Mixture of Depths Made Easy
Chen Zhang, Meizhi Zhong, Qimeng Wang +8
Long-context efficiency has recently become a trending topic in serving large language models (LLMs). And mixture of depths (MoD) is proposed as a perfect fit to bring down both la…
Beyond the Speculative Game: A Survey of Speculative Execution in Large Language Models
Chen Zhang, Zhuorui Liu, Dawei Song
With the increasingly giant scales of (causal) large language models (LLMs), the inference efficiency comes as one of the core concerns along the improved performance. In contrast…
MiniDisc: Minimal Distillation Schedule for Language Model Compression
Chen Zhang, Yang Yang, Qifan Wang +4
Recent studies have uncovered that language model distillation is less effective when facing a large capacity gap between the teacher and the student, and introduced teacher assist…