LightSeq: Sequence Level Parallelism for Distributed Training of Long Context Transformers
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2310.03294
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Published
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2
Hao AI Lab
university
AI & ML interests
We are passionate about designing strong, efficient, and secure machine learning models and algorithms, and in building scalable, practical distributed systems that can support real-world machine learning workloads.
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zhisbug
authored
17
papers
about 1 month ago
Online Speculative Decoding
Paper
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2310.07177
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Published
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1
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
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2306.05685
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Published
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33
Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks
Paper
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2306.13103
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Published
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2
DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving
Paper
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2401.09670
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Published
Break the Sequential Dependency of LLM Inference Using Lookahead Decoding
Paper
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2402.02057
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Published
Efficient Memory Management for Large Language Model Serving with PagedAttention
Paper
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2309.06180
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Published
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25
CLLMs: Consistency Large Language Models
Paper
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2403.00835
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Published
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3
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
Paper
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2403.04132
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Published
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38
Toward Inference-optimal Mixture-of-Expert Large Language Models
Paper
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2404.02852
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Published
Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
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2404.08801
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Published
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66
MPCFormer: fast, performant and private Transformer inference with MPC
Paper
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2211.01452
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Published
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1
Optimizing Speculative Decoding for Serving Large Language Models Using Goodput
Paper
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2406.14066
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Published
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1
AdaMoE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models
Paper
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2406.13233
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Published
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1
Efficient LLM Scheduling by Learning to Rank
Paper
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2408.15792
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Published
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20
Specifications: The missing link to making the development of LLM systems an engineering discipline
Paper
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2412.05299
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Published
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1
Efficiently Serving LLM Reasoning Programs with Certaindex
Paper
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2412.20993
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Published
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35