Collections
Discover the best community collections!
Collections trending this week
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Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 47 -
Round and Round We Go! What makes Rotary Positional Encodings useful?
Paper • 2410.06205 • Published • 2 -
Byte Latent Transformer: Patches Scale Better Than Tokens
Paper • 2412.09871 • Published • 109 -
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
Paper • 2410.20771 • Published • 3
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Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 47 -
2D Matryoshka Sentence Embeddings
Paper • 2402.14776 • Published • 8 -
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 169
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Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 47 -
2D Matryoshka Sentence Embeddings
Paper • 2402.14776 • Published • 8 -
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 169
-
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 47 -
Round and Round We Go! What makes Rotary Positional Encodings useful?
Paper • 2410.06205 • Published • 2 -
Byte Latent Transformer: Patches Scale Better Than Tokens
Paper • 2412.09871 • Published • 109 -
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
Paper • 2410.20771 • Published • 3