Collections
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Collections including paper arxiv:2412.15115
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Attention Is All You Need
Paper • 1706.03762 • Published • 122 -
Scaling Laws for Neural Language Models
Paper • 2001.08361 • Published • 10 -
Training Compute-Optimal Large Language Models
Paper • 2203.15556 • Published • 11 -
Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
Paper • 2210.04186 • Published
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Attention Is All You Need
Paper • 1706.03762 • Published • 122 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 23 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 251
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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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A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale
Paper • 2309.06497 • Published • 7 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 629 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 251
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Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
Paper • 2511.22699 • Published • 245 -
A Survey on Diffusion Language Models
Paper • 2508.10875 • Published • 34 -
Scalable Diffusion Models with Transformers
Paper • 2212.09748 • Published • 17 -
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Paper • 2403.03206 • Published • 71
-
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
-
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale
Paper • 2309.06497 • Published • 7 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 629 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 251
-
Attention Is All You Need
Paper • 1706.03762 • Published • 122 -
Scaling Laws for Neural Language Models
Paper • 2001.08361 • Published • 10 -
Training Compute-Optimal Large Language Models
Paper • 2203.15556 • Published • 11 -
Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
Paper • 2210.04186 • Published
-
Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
Paper • 2511.22699 • Published • 245 -
A Survey on Diffusion Language Models
Paper • 2508.10875 • Published • 34 -
Scalable Diffusion Models with Transformers
Paper • 2212.09748 • Published • 17 -
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Paper • 2403.03206 • Published • 71
-
Attention Is All You Need
Paper • 1706.03762 • Published • 122 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 23 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 251