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Dec 16, 2023
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Sparse Backpropagation for MoE Training
Paper • 2310.00811 • Published • 3 -
The Forward-Forward Algorithm: Some Preliminary Investigations
Paper • 2212.13345 • Published • 5 -
Fine-Tuning Language Models with Just Forward Passes
Paper • 2305.17333 • Published • 4 -
Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
Paper • 2309.13192 • Published • 1
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A Unified View of Long-Sequence Models towards Modeling Million-Scale Dependencies
Paper • 2302.06218 • Published • 1 -
ZeRO++: Extremely Efficient Collective Communication for Giant Model Training
Paper • 2306.10209 • Published • 2 -
SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System
Paper • 2205.10034 • Published • 1 -
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
Paper • 2303.06318 • Published • 2
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-
Sparse Backpropagation for MoE Training
Paper • 2310.00811 • Published • 3 -
The Forward-Forward Algorithm: Some Preliminary Investigations
Paper • 2212.13345 • Published • 5 -
Fine-Tuning Language Models with Just Forward Passes
Paper • 2305.17333 • Published • 4 -
Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
Paper • 2309.13192 • Published • 1
Collection by Jan 3, 2024
-
A Unified View of Long-Sequence Models towards Modeling Million-Scale Dependencies
Paper • 2302.06218 • Published • 1 -
ZeRO++: Extremely Efficient Collective Communication for Giant Model Training
Paper • 2306.10209 • Published • 2 -
SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System
Paper • 2205.10034 • Published • 1 -
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
Paper • 2303.06318 • Published • 2
Collection by Oct 29, 2023
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