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Fourier Position Embedding: Enhancing Attention's Periodic Extension for Length Generalization
Paper • 2412.17739 • Published • 41 -
SmoothQuant+: Accurate and Efficient 4-bit Post-Training WeightQuantization for LLM
Paper • 2312.03788 • Published • 1 -
FlatQuant: Flatness Matters for LLM Quantization
Paper • 2410.09426 • Published • 16 -
FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
Paper • 2501.01005 • Published • 1
Collections
Discover the best community collections!
Collections including paper arxiv:2410.09426
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CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data
Paper • 2404.15653 • Published • 29 -
MoDE: CLIP Data Experts via Clustering
Paper • 2404.16030 • Published • 15 -
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
Paper • 2405.12130 • Published • 50 -
Reducing Transformer Key-Value Cache Size with Cross-Layer Attention
Paper • 2405.12981 • Published • 33
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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
Orion-14B: Open-source Multilingual Large Language Models
Paper • 2401.12246 • Published • 14 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 60 -
MM-LLMs: Recent Advances in MultiModal Large Language Models
Paper • 2401.13601 • Published • 48
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LinFusion: 1 GPU, 1 Minute, 16K Image
Paper • 2409.02097 • Published • 34 -
Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion
Paper • 2409.11406 • Published • 27 -
Diffusion Models Are Real-Time Game Engines
Paper • 2408.14837 • Published • 126 -
Segment Anything with Multiple Modalities
Paper • 2408.09085 • Published • 22
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 625 -
BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 105 -
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 107 -
TransformerFAM: Feedback attention is working memory
Paper • 2404.09173 • Published • 43
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SqueezeLLM: Dense-and-Sparse Quantization
Paper • 2306.07629 • Published • 4 -
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
Paper • 2309.02784 • Published • 2 -
Extreme Compression of Large Language Models via Additive Quantization
Paper • 2401.06118 • Published • 13 -
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper • 2402.04291 • Published • 50
-
Fourier Position Embedding: Enhancing Attention's Periodic Extension for Length Generalization
Paper • 2412.17739 • Published • 41 -
SmoothQuant+: Accurate and Efficient 4-bit Post-Training WeightQuantization for LLM
Paper • 2312.03788 • Published • 1 -
FlatQuant: Flatness Matters for LLM Quantization
Paper • 2410.09426 • Published • 16 -
FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
Paper • 2501.01005 • Published • 1
-
LinFusion: 1 GPU, 1 Minute, 16K Image
Paper • 2409.02097 • Published • 34 -
Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion
Paper • 2409.11406 • Published • 27 -
Diffusion Models Are Real-Time Game Engines
Paper • 2408.14837 • Published • 126 -
Segment Anything with Multiple Modalities
Paper • 2408.09085 • Published • 22
-
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data
Paper • 2404.15653 • Published • 29 -
MoDE: CLIP Data Experts via Clustering
Paper • 2404.16030 • Published • 15 -
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
Paper • 2405.12130 • Published • 50 -
Reducing Transformer Key-Value Cache Size with Cross-Layer Attention
Paper • 2405.12981 • Published • 33
-
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 625 -
BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 105 -
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 107 -
TransformerFAM: Feedback attention is working memory
Paper • 2404.09173 • Published • 43
-
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
Orion-14B: Open-source Multilingual Large Language Models
Paper • 2401.12246 • Published • 14 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 60 -
MM-LLMs: Recent Advances in MultiModal Large Language Models
Paper • 2401.13601 • Published • 48
-
SqueezeLLM: Dense-and-Sparse Quantization
Paper • 2306.07629 • Published • 4 -
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
Paper • 2309.02784 • Published • 2 -
Extreme Compression of Large Language Models via Additive Quantization
Paper • 2401.06118 • Published • 13 -
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper • 2402.04291 • Published • 50