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Mar 2
Mar 2
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Discover the best community collections!
Collections including paper arxiv:2402.19427
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Apr 24
Apr 24
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Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 49 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 52 -
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks
Paper • 2403.00522 • Published • 44 -
Resonance RoPE: Improving Context Length Generalization of Large Language Models
Paper • 2403.00071 • Published • 22
Collection by
10 days ago
10 days ago
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Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 52 -
Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 49 -
StarCoder 2 and The Stack v2: The Next Generation
Paper • 2402.19173 • Published • 132 -
Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 18
Collection by
May 22
May 22
Collection by
1 day ago
1 day ago
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Nemotron-4 15B Technical Report
Paper • 2402.16819 • Published • 42 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 52 -
RWKV: Reinventing RNNs for the Transformer Era
Paper • 2305.13048 • Published • 12 -
Reformer: The Efficient Transformer
Paper • 2001.04451 • Published
Collection by
Jul 15
Jul 15
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 590 -
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
Paper • 2402.17177 • Published • 88 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 52 -
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
Paper • 2402.19479 • Published • 32
Collection by
16 days ago
16 days ago
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 590 -
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 182 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 52 -
ResLoRA: Identity Residual Mapping in Low-Rank Adaption
Paper • 2402.18039 • Published • 11
Collection by
Mar 7
Mar 7
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Graph Mamba: Towards Learning on Graphs with State Space Models
Paper • 2402.08678 • Published • 13 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 28 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 48 -
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Paper • 2401.09417 • Published • 58
Collection by
Apr 13
Apr 13
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Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 18 -
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Paper • 2402.10644 • Published • 78 -
Repeat After Me: Transformers are Better than State Space Models at Copying
Paper • 2402.01032 • Published • 22 -
Zoology: Measuring and Improving Recall in Efficient Language Models
Paper • 2312.04927 • Published • 2
Collection by
23 days ago
23 days ago
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Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 52 -
Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 18 -
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition
Paper • 2402.15220 • Published • 19 -
Linear Transformers are Versatile In-Context Learners
Paper • 2402.14180 • Published • 6