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Collections including paper arxiv:2404.07143

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Paper • 2312.00752 • Published Dec 1, 2023 • 138
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 • 132

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 • 110
LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration

Paper • 2402.11550 • Published Feb 18 • 15
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

Toward Joint Language Modeling for Speech Units and Text

Paper • 2310.08715 • Published Oct 12, 2023 • 7
facebook/wav2vec2-large-xlsr-53

Updated Mar 18, 2022 • 81.5k • 109
bczhou/TinyLLaVA-1.5B

Image-Text-to-Text • Updated Jun 14 • 332 • 16
Running on T4

368

🔥

LLaVA

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 • 590
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 • 103
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

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

Paper • 2404.07143 • Published Apr 10 • 103
RULER: What's the Real Context Size of Your Long-Context Language Models?

Paper • 2404.06654 • Published Apr 9 • 33

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