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Apr 16, 2025
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Discover the best community collections!
Collections including paper arxiv:2410.06205
Collection by
Dec 25, 2024
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The Impact of Positional Encoding on Length Generalization in Transformers
Paper • 2305.19466 • Published • 2 -
Transformers Can Do Arithmetic with the Right Embeddings
Paper • 2405.17399 • Published • 54 -
Teaching Transformers Causal Reasoning through Axiomatic Training
Paper • 2407.07612 • Published • 2 -
Round and Round We Go! What makes Rotary Positional Encodings useful?
Paper • 2410.06205 • Published • 2
Collection by
Nov 22, 2024
Collection by
Nov 22, 2024
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Resonance RoPE: Improving Context Length Generalization of Large Language Models
Paper • 2403.00071 • Published • 24 -
Scaling Laws of RoPE-based Extrapolation
Paper • 2310.05209 • Published • 8 -
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
Paper • 2404.12387 • Published • 39 -
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
Paper • 2404.14619 • Published • 126
Collection by
Nov 22, 2024
Collection by
Dec 25, 2024
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The Impact of Positional Encoding on Length Generalization in Transformers
Paper • 2305.19466 • Published • 2 -
Qwen2 Technical Report
Paper • 2407.10671 • Published • 168 -
Round and Round We Go! What makes Rotary Positional Encodings useful?
Paper • 2410.06205 • Published • 2 -
ThunderKittens: Simple, Fast, and Adorable AI Kernels
Paper • 2410.20399 • Published • 2
Collection by
Jan 30, 2025
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Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 48 -
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 • 108 -
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
Paper • 2410.20771 • Published • 3
Collection by
Apr 16, 2025
Collection by
Nov 22, 2024
Collection by
Dec 25, 2024
-
The Impact of Positional Encoding on Length Generalization in Transformers
Paper • 2305.19466 • Published • 2 -
Transformers Can Do Arithmetic with the Right Embeddings
Paper • 2405.17399 • Published • 54 -
Teaching Transformers Causal Reasoning through Axiomatic Training
Paper • 2407.07612 • Published • 2 -
Round and Round We Go! What makes Rotary Positional Encodings useful?
Paper • 2410.06205 • Published • 2
Collection by
Dec 25, 2024
-
The Impact of Positional Encoding on Length Generalization in Transformers
Paper • 2305.19466 • Published • 2 -
Qwen2 Technical Report
Paper • 2407.10671 • Published • 168 -
Round and Round We Go! What makes Rotary Positional Encodings useful?
Paper • 2410.06205 • Published • 2 -
ThunderKittens: Simple, Fast, and Adorable AI Kernels
Paper • 2410.20399 • Published • 2
Collection by
Nov 22, 2024
Collection by
Jan 30, 2025
-
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 48 -
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 • 108 -
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
Paper • 2410.20771 • Published • 3
Collection by
Nov 22, 2024
-
Resonance RoPE: Improving Context Length Generalization of Large Language Models
Paper • 2403.00071 • Published • 24 -
Scaling Laws of RoPE-based Extrapolation
Paper • 2310.05209 • Published • 8 -
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
Paper • 2404.12387 • Published • 39 -
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
Paper • 2404.14619 • Published • 126