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ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
Paper • 2403.03853 • Published • 66 -
SliceGPT: Compress Large Language Models by Deleting Rows and Columns
Paper • 2401.15024 • Published • 74 -
Your Transformer is Secretly Linear
Paper • 2405.12250 • Published • 158 -
Yi: Open Foundation Models by 01.AI
Paper • 2403.04652 • Published • 65
Collections
Discover the best community collections!
Collections including paper arxiv:2403.17887
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Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 111 -
The Unreasonable Ineffectiveness of the Deeper Layers
Paper • 2403.17887 • Published • 82 -
Tuning Language Models by Proxy
Paper • 2401.08565 • Published • 22 -
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning
Paper • 2402.04833 • Published • 5
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One-step Diffusion with Distribution Matching Distillation
Paper • 2311.18828 • Published • 3 -
The Unreasonable Ineffectiveness of the Deeper Layers
Paper • 2403.17887 • Published • 82 -
Condition-Aware Neural Network for Controlled Image Generation
Paper • 2404.01143 • Published • 13 -
Locating and Editing Factual Associations in GPT
Paper • 2202.05262 • Published • 1
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Attention Is All You Need
Paper • 1706.03762 • Published • 92 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 18 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 21 -
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
Paper • 2407.21770 • Published • 22
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The Unreasonable Ineffectiveness of the Deeper Layers
Paper • 2403.17887 • Published • 82 -
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 107 -
ReFT: Representation Finetuning for Language Models
Paper • 2404.03592 • Published • 101 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 62
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ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
Paper • 2403.03853 • Published • 66 -
SliceGPT: Compress Large Language Models by Deleting Rows and Columns
Paper • 2401.15024 • Published • 74 -
Your Transformer is Secretly Linear
Paper • 2405.12250 • Published • 158 -
Yi: Open Foundation Models by 01.AI
Paper • 2403.04652 • Published • 65
-
Attention Is All You Need
Paper • 1706.03762 • Published • 92 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 18 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 21 -
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
Paper • 2407.21770 • Published • 22
-
The Unreasonable Ineffectiveness of the Deeper Layers
Paper • 2403.17887 • Published • 82 -
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 107 -
ReFT: Representation Finetuning for Language Models
Paper • 2404.03592 • Published • 101 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 62
-
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 111 -
The Unreasonable Ineffectiveness of the Deeper Layers
Paper • 2403.17887 • Published • 82 -
Tuning Language Models by Proxy
Paper • 2401.08565 • Published • 22 -
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning
Paper • 2402.04833 • Published • 5
-
One-step Diffusion with Distribution Matching Distillation
Paper • 2311.18828 • Published • 3 -
The Unreasonable Ineffectiveness of the Deeper Layers
Paper • 2403.17887 • Published • 82 -
Condition-Aware Neural Network for Controlled Image Generation
Paper • 2404.01143 • Published • 13 -
Locating and Editing Factual Associations in GPT
Paper • 2202.05262 • Published • 1