RACE-h
1개 벤치마크 · 논문 2편 · 이 태스크의 논문 보기 →
Benchmarks
BIG-bench
결과 1개
Most implemented
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
2021-12-08 · 구현 3개
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
2024-04-25 · 구현 1개
Papers
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
2024-04-25
· Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer 외
We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for la…
GSM8KHellaSwagHumanEvalMath+6Scaling Language Models: Methods, Analysis & Insights from Training Gopher
2021-12-08 · NA 2021 12
· Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican 외
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis o…
Abstract AlgebraAnachronismsAnalogical SimilarityAnalytic Entailment+143