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Oct 25, 2024
Oct 25, 2024Collections
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Collections including paper arxiv:2409.17066
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Oct 1, 2024
Oct 1, 2024Collection by
1 day ago
1 day ago-
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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2 days ago
2 days ago-
Chain-of-Verification Reduces Hallucination in Large Language Models
Paper • 2309.11495 • Published • 39 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 81 -
CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages
Paper • 2309.09400 • Published • 85 -
Language Modeling Is Compression
Paper • 2309.10668 • Published • 83
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Oct 8, 2024
Oct 8, 2024Collection by
Oct 8, 2024
Oct 8, 2024-
How to Train Data-Efficient LLMs
Paper • 2402.09668 • Published • 42 -
SliceGPT: Compress Large Language Models by Deleting Rows and Columns
Paper • 2401.15024 • Published • 74 -
SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning
Paper • 2407.07523 • Published • 6 -
Spectra: A Comprehensive Study of Ternary, Quantized, and FP16 Language Models
Paper • 2407.12327 • Published • 79
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Sep 26
Sep 26-
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 626 -
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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Oct 25, 2024
Oct 25, 2024Collection by
Oct 8, 2024
Oct 8, 2024Collection by
Oct 1, 2024
Oct 1, 2024Collection by
Oct 8, 2024
Oct 8, 2024-
How to Train Data-Efficient LLMs
Paper • 2402.09668 • Published • 42 -
SliceGPT: Compress Large Language Models by Deleting Rows and Columns
Paper • 2401.15024 • Published • 74 -
SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning
Paper • 2407.07523 • Published • 6 -
Spectra: A Comprehensive Study of Ternary, Quantized, and FP16 Language Models
Paper • 2407.12327 • Published • 79
Collection by
1 day ago
1 day ago-
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
Collection by
Sep 26
Sep 26-
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 626 -
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
Collection by
2 days ago
2 days ago-
Chain-of-Verification Reduces Hallucination in Large Language Models
Paper • 2309.11495 • Published • 39 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 81 -
CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages
Paper • 2309.09400 • Published • 85 -
Language Modeling Is Compression
Paper • 2309.10668 • Published • 83