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Collections including paper arxiv:2404.07143

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MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

Paper • 2403.09611 • Published Mar 14 • 123
Evolutionary Optimization of Model Merging Recipes

Paper • 2403.13187 • Published Mar 19 • 49
MobileVLM V2: Faster and Stronger Baseline for Vision Language Model

Paper • 2402.03766 • Published Feb 6 • 12
LLM Agent Operating System

Paper • 2403.16971 • Published Mar 25 • 64

Long context LLM

Sequence Parallelism: Long Sequence Training from System Perspective

Paper • 2105.13120 • Published May 26, 2021 • 5
Ring Attention with Blockwise Transformers for Near-Infinite Context

Paper • 2310.01889 • Published Oct 3, 2023 • 9
Striped Attention: Faster Ring Attention for Causal Transformers

Paper • 2311.09431 • Published Nov 15, 2023 • 4
DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Paper • 2309.14509 • Published Sep 25, 2023 • 17

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Paper • 2312.00752 • Published Dec 1, 2023 • 135
Elucidating the Design Space of Diffusion-Based Generative Models

Paper • 2206.00364 • Published Jun 1, 2022 • 13
GLU Variants Improve Transformer

Paper • 2002.05202 • Published Feb 12, 2020 • 1
StarCoder 2 and The Stack v2: The Next Generation

Paper • 2402.19173 • Published Feb 29 • 130

LLoCO: Learning Long Contexts Offline

Paper • 2404.07979 • Published Apr 11 • 19
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens

Paper • 2402.13753 • Published Feb 21 • 109
LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration

Paper • 2402.11550 • Published Feb 18 • 14
LongAlign: A Recipe for Long Context Alignment of Large Language Models

Paper • 2401.18058 • Published Jan 31 • 21

Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Paper • 2404.07143 • Published Apr 10 • 103

Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Paper • 2404.08801 • Published Apr 12 • 62
Ring Attention with Blockwise Transformers for Near-Infinite Context

Paper • 2310.01889 • Published Oct 3, 2023 • 9
World Model on Million-Length Video And Language With RingAttention

Paper • 2402.08268 • Published Feb 13 • 36
Scaling Transformer to 1M tokens and beyond with RMT

Paper • 2304.11062 • Published Apr 19, 2023 • 2

Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Paper • 2404.08801 • Published Apr 12 • 62
TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14 • 43
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Paper • 2404.07143 • Published Apr 10 • 103
Block Transformer: Global-to-Local Language Modeling for Fast Inference

Paper • 2406.02657 • Published Jun 4 • 36

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 587
BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 96
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Paper • 2404.02258 • Published Apr 2 • 102
TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14 • 43

Transformers_papers

Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Paper • 2404.07143 • Published Apr 10 • 103
Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Paper • 2404.08801 • Published Apr 12 • 62

Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Paper • 2404.07143 • Published Apr 10 • 103

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