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Attention Is All You Need
Paper • 1706.03762 • Published • 96 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 53 -
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
Paper • 2101.03961 • Published • 13 -
Proximal Policy Optimization Algorithms
Paper • 1707.06347 • Published • 11
Collections
Discover the best community collections!
Collections including paper arxiv:2112.06905
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Nemotron-4 15B Technical Report
Paper • 2402.16819 • Published • 46 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 56 -
RWKV: Reinventing RNNs for the Transformer Era
Paper • 2305.13048 • Published • 19 -
Reformer: The Efficient Transformer
Paper • 2001.04451 • Published
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QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models
Paper • 2310.16795 • Published • 27 -
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
Paper • 2308.12066 • Published • 4 -
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
Paper • 2303.06182 • Published • 1 -
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate
Paper • 2112.14397 • Published • 1
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Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 146 -
Elucidating the Design Space of Diffusion-Based Generative Models
Paper • 2206.00364 • Published • 18 -
GLU Variants Improve Transformer
Paper • 2002.05202 • Published • 4 -
StarCoder 2 and The Stack v2: The Next Generation
Paper • 2402.19173 • Published • 151
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Attention Is All You Need
Paper • 1706.03762 • Published • 96 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 23 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 9 -
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Paper • 1910.01108 • Published • 21
-
Attention Is All You Need
Paper • 1706.03762 • Published • 96 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 53 -
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
Paper • 2101.03961 • Published • 13 -
Proximal Policy Optimization Algorithms
Paper • 1707.06347 • Published • 11
-
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 146 -
Elucidating the Design Space of Diffusion-Based Generative Models
Paper • 2206.00364 • Published • 18 -
GLU Variants Improve Transformer
Paper • 2002.05202 • Published • 4 -
StarCoder 2 and The Stack v2: The Next Generation
Paper • 2402.19173 • Published • 151
-
Nemotron-4 15B Technical Report
Paper • 2402.16819 • Published • 46 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 56 -
RWKV: Reinventing RNNs for the Transformer Era
Paper • 2305.13048 • Published • 19 -
Reformer: The Efficient Transformer
Paper • 2001.04451 • Published
-
Attention Is All You Need
Paper • 1706.03762 • Published • 96 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 23 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 9 -
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Paper • 1910.01108 • Published • 21
-
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models
Paper • 2310.16795 • Published • 27 -
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
Paper • 2308.12066 • Published • 4 -
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
Paper • 2303.06182 • Published • 1 -
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate
Paper • 2112.14397 • Published • 1