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Collections including paper arxiv:2312.17238

Papers - MoE - Training

Robust Mixture-of-Expert Training for Convolutional Neural Networks

Paper • 2308.10110 • Published Aug 19, 2023 • 2
Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Paper • 2308.06093 • Published Aug 11, 2023 • 2
ConstitutionalExperts: Training a Mixture of Principle-based Prompts

Paper • 2403.04894 • Published Mar 7 • 2
Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models

Paper • 2403.03432 • Published Mar 6 • 1

EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty

Paper • 2401.15077 • Published Jan 26 • 18
Fast Inference of Mixture-of-Experts Language Models with Offloading

Paper • 2312.17238 • Published Dec 28, 2023 • 7

AnimateLCM: Accelerating the Animation of Personalized Diffusion Models and Adapters with Decoupled Consistency Learning

Paper • 2402.00769 • Published Feb 1 • 20
LCM-LoRA: A Universal Stable-Diffusion Acceleration Module

Paper • 2311.05556 • Published Nov 9, 2023 • 80
LongAlign: A Recipe for Long Context Alignment of Large Language Models

Paper • 2401.18058 • Published Jan 31 • 21
Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 16

Fast Inference of Mixture-of-Experts Language Models with Offloading

Paper • 2312.17238 • Published Dec 28, 2023 • 7

mixture-of-experts

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 4
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 4
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

MoEs papers reading list

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 4
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 4
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

S^{3}: Increasing GPU Utilization during Generative Inference for Higher Throughput

Paper • 2306.06000 • Published Jun 9, 2023 • 1
Fast Distributed Inference Serving for Large Language Models

Paper • 2305.05920 • Published May 10, 2023 • 1
Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline

Paper • 2305.13144 • Published May 22, 2023 • 1
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

Paper • 2303.06182 • Published Mar 10, 2023 • 1

QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 26
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

Paper • 2308.12066 • Published Aug 23, 2023 • 4
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

Paper • 2303.06182 • Published Mar 10, 2023 • 1
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

Paper • 2112.14397 • Published Dec 29, 2021 • 1

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