3 citations · 7 across the 4 of their papers we have counts for
4 papers
Privacy-Preserving Transformers: SwiftKey's Differential Privacy Implementation
Abdelrahman Abouelenin, Mohamed Abdelrehim, Raffy Fahim +2
In this paper we train a transformer using differential privacy (DP) for language modeling in SwiftKey. We run multiple experiments to balance the trade-off between the model size,…
Mixture of Quantized Experts (MoQE): Complementary Effect of Low-bit Quantization and Robustness
Young Jin Kim, Raffy Fahim, Hany Hassan Awadalla
Large Mixture of Experts (MoE) models could achieve state-of-the-art quality on various language tasks, including machine translation task, thanks to the efficient model scaling ca…
FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs
Young Jin Kim, Rawn Henry, Raffy Fahim +1
Large Language Models (LLMs) have achieved state-of-the-art performance across various language tasks but pose challenges for practical deployment due to their substantial memory r…
Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production
Young Jin Kim, Rawn Henry, Raffy Fahim +1
Mixture of Experts (MoE) models with conditional execution of sparsely activated layers have enabled training models with a much larger number of parameters. As a result, these mod…