HEAPr Introduces Atomic Expert Pruning for Efficient LLMs

HEAPr (Hessian-based Efficient Atomic Expert Pruning) trims MoE models by 20%‑25% and cuts FLOPs 20% using atomic expert scores, with two forward passes and one backward pass on a set. Read more: getnews.me/heapr-introduces-atomic-... #heapr #moe #modelpruning

TLDR; FuseGPT
Large Language Models are huge, to reduce their resource requirements an common approach is to prune uneccesary layers. However this is not without cost, it requires expensive retraining to retain lost performance.

#AI #LLM #GPT #ModelPruning