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Collections including paper arxiv:2402.19427

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18, 2024 • 151
Tuning Language Models by Proxy

Paper • 2401.08565 • Published Jan 16, 2024 • 22
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11, 2024 • 56

Large Language Models

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28, 2024 • 20

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
Running on Zero

221

Microsoft Phi-3-Vision-128k

😻

221

Generate text descriptions from images

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

Paper • 2402.17764 • Published Feb 27, 2024 • 625
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Paper • 2402.17177 • Published Feb 27, 2024 • 88
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers

Paper • 2402.19479 • Published Feb 29, 2024 • 35

Mambas and LLM-AltArch

Graph Mamba: Towards Learning on Graphs with State Space Models

Paper • 2402.08678 • Published Feb 13, 2024 • 17
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6, 2024 • 32
MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24, 2024 • 60
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Paper • 2401.09417 • Published Jan 17, 2024 • 62

StarCoder 2 and The Stack v2: The Next Generation

Paper • 2402.19173 • Published Feb 29, 2024 • 151
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28, 2024 • 20
Priority Sampling of Large Language Models for Compilers

Paper • 2402.18734 • Published Feb 28, 2024 • 19

Beyond Language Models: Byte Models are Digital World Simulators

Paper • 2402.19155 • Published Feb 29, 2024 • 53
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks

Paper • 2403.00522 • Published Mar 1, 2024 • 46
Resonance RoPE: Improving Context Length Generalization of Large Language Models

Paper • 2403.00071 • Published Feb 29, 2024 • 24

LLM_architectures

Nemotron-4 15B Technical Report

Paper • 2402.16819 • Published Feb 26, 2024 • 46
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
RWKV: Reinventing RNNs for the Transformer Era

Paper • 2305.13048 • Published May 22, 2023 • 19
Reformer: The Efficient Transformer

Paper • 2001.04451 • Published Jan 13, 2020

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

Paper • 2402.17764 • Published Feb 27, 2024 • 625
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Paper • 2403.03507 • Published Mar 6, 2024 • 189
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
ResLoRA: Identity Residual Mapping in Low-Rank Adaption

Paper • 2402.18039 • Published Feb 28, 2024 • 11

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28, 2024 • 20
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16, 2024 • 81
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1, 2024 • 24
Zoology: Measuring and Improving Recall in Efficient Language Models

Paper • 2312.04927 • Published Dec 8, 2023 • 2

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18, 2024 • 151
Tuning Language Models by Proxy

Paper • 2401.08565 • Published Jan 16, 2024 • 22
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11, 2024 • 56

StarCoder 2 and The Stack v2: The Next Generation

Paper • 2402.19173 • Published Feb 29, 2024 • 151
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28, 2024 • 20
Priority Sampling of Large Language Models for Compilers

Paper • 2402.18734 • Published Feb 28, 2024 • 19

Large Language Models

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28, 2024 • 20

Beyond Language Models: Byte Models are Digital World Simulators

Paper • 2402.19155 • Published Feb 29, 2024 • 53
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks

Paper • 2403.00522 • Published Mar 1, 2024 • 46
Resonance RoPE: Improving Context Length Generalization of Large Language Models

Paper • 2403.00071 • Published Feb 29, 2024 • 24

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
Running on Zero

221

Microsoft Phi-3-Vision-128k

😻

221

Generate text descriptions from images

LLM_architectures

Nemotron-4 15B Technical Report

Paper • 2402.16819 • Published Feb 26, 2024 • 46
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
RWKV: Reinventing RNNs for the Transformer Era

Paper • 2305.13048 • Published May 22, 2023 • 19
Reformer: The Efficient Transformer

Paper • 2001.04451 • Published Jan 13, 2020

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

Paper • 2402.17764 • Published Feb 27, 2024 • 625
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Paper • 2402.17177 • Published Feb 27, 2024 • 88
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers

Paper • 2402.19479 • Published Feb 29, 2024 • 35

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

Paper • 2402.17764 • Published Feb 27, 2024 • 625
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Paper • 2403.03507 • Published Mar 6, 2024 • 189
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 56
ResLoRA: Identity Residual Mapping in Low-Rank Adaption

Paper • 2402.18039 • Published Feb 28, 2024 • 11

Mambas and LLM-AltArch

Graph Mamba: Towards Learning on Graphs with State Space Models

Paper • 2402.08678 • Published Feb 13, 2024 • 17
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6, 2024 • 32
MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24, 2024 • 60
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Paper • 2401.09417 • Published Jan 17, 2024 • 62

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28, 2024 • 20
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16, 2024 • 81
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1, 2024 • 24
Zoology: Measuring and Improving Recall in Efficient Language Models

Paper • 2312.04927 • Published Dec 8, 2023 • 2

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