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taesiri  submitted a paper 22 days ago
Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference
melhoushi  authored a paper about 1 month ago
Calibrating Beyond English: Language Diversity for Better Quantized Multilingual LLM
melhoushi  authored a paper about 1 month ago
Gated Recurrent Transformers: Expressive Depth through Recurrent Modulation
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Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference

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Cerebras is the inventor of the Wafer-Scale Engine – the revolutionary processor at the heart of our Cerebras CS-X systems.

Our co-designed hardware/software stack enables training large language models upward of 1 trillion parameters using only data parallelism. Our massive on-chip memory enables us to fit all model weights in SRAM and run the World’s Fastest AI Inference.

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Cerebras REAP
Sparse MoE models compressed using REAP (Router-weighted Expert Activation Pruning) method
Cerebras REAP
Sparse MoE models compressed using REAP (Router-weighted Expert Activation Pruning) method
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