Search Results for author: Ying Sheng

Found 14 papers, 12 papers with code

Fairness in Serving Large Language Models

1 code implementation31 Dec 2023 Ying Sheng, Shiyi Cao, Dacheng Li, Banghua Zhu, Zhuohan Li, Danyang Zhuo, Joseph E. Gonzalez, Ion Stoica

High-demand LLM inference services (e. g., ChatGPT and BARD) support a wide range of requests from short chat conversations to long document reading.

Fairness Scheduling

Efficiently Programming Large Language Models using SGLang

1 code implementation12 Dec 2023 Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Jeff Huang, Chuyue Sun, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark Barrett, Ying Sheng

SGLang is designed for the efficient programming of LLMs and incorporates primitives for common LLM programming patterns.

S-LoRA: Serving Thousands of Concurrent LoRA Adapters

1 code implementation6 Nov 2023 Ying Sheng, Shiyi Cao, Dacheng Li, Coleman Hooper, Nicholas Lee, Shuo Yang, Christopher Chou, Banghua Zhu, Lianmin Zheng, Kurt Keutzer, Joseph E. Gonzalez, Ion Stoica

To capitalize on these opportunities, we present S-LoRA, a system designed for the scalable serving of many LoRA adapters.

Clover: Closed-Loop Verifiable Code Generation

1 code implementation26 Oct 2023 Chuyue Sun, Ying Sheng, Oded Padon, Clark Barrett

The use of large language models for code generation is a rapidly growing trend in software development.

Code Generation

LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset

1 code implementation21 Sep 2023 Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric. P Xing, Joseph E. Gonzalez, Ion Stoica, Hao Zhang

Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications.

Chatbot Instruction Following

Efficient Memory Management for Large Language Model Serving with PagedAttention

4 code implementations12 Sep 2023 Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, Ion Stoica

On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage.

Language Modelling Large Language Model +1

H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

1 code implementation24 Jun 2023 Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, Zhangyang Wang, Beidi Chen

Based on these insights, we propose Heavy Hitter Oracle (H$_2$O), a KV cache eviction policy that dynamically retains a balance of recent and H$_2$ tokens.

Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

5 code implementations NeurIPS 2023 Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, Ion Stoica

Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences.

Chatbot Language Modelling +1

On Optimal Caching and Model Multiplexing for Large Model Inference

1 code implementation3 Jun 2023 Banghua Zhu, Ying Sheng, Lianmin Zheng, Clark Barrett, Michael I. Jordan, Jiantao Jiao

Theoretically, we provide an optimal algorithm for jointly optimizing both approaches to reduce the inference cost in both offline and online tabular settings.

FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

1 code implementation13 Mar 2023 Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang

As a result, when running OPT-175B on a single 16GB GPU, FlexGen achieves significantly higher throughput compared to state-of-the-art offloading systems, reaching a generation throughput of 1 token/s for the first time with an effective batch size of 144.

Language Modelling Large Language Model

AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving

2 code implementations22 Feb 2023 Zhuohan Li, Lianmin Zheng, Yinmin Zhong, Vincent Liu, Ying Sheng, Xin Jin, Yanping Huang, Zhifeng Chen, Hao Zhang, Joseph E. Gonzalez, Ion Stoica

Model parallelism is conventionally viewed as a method to scale a single large deep learning model beyond the memory limits of a single device.

Simplified DOM Trees for Transferable Attribute Extraction from the Web

2 code implementations7 Jan 2021 Yichao Zhou, Ying Sheng, Nguyen Vo, Nick Edmonds, Sandeep Tata

There has been a steady need to precisely extract structured knowledge from the web (i. e. HTML documents).

Attribute Attribute Extraction +1

FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents

no code implementations21 Oct 2020 Bill Yuchen Lin, Ying Sheng, Nguyen Vo, Sandeep Tata

By combining these stages, FreeDOM is able to generalize to unseen sites after training on a small number of seed sites from that vertical without requiring expensive hand-crafted features over visual renderings of the page.

Subspace Embedding and Linear Regression with Orlicz Norm

no code implementations ICML 2018 Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong

An Orlicz norm is parameterized by a non-negative convex function $G:\mathbb{R}_+\rightarrow\mathbb{R}_+$ with $G(0)=0$: the Orlicz norm of a vector $x\in\mathbb{R}^n$ is defined as $ \|x\|_G=\inf\left\{\alpha>0\large\mid\sum_{i=1}^n G(|x_i|/\alpha)\leq 1\right\}.


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