Gịnị bụ Ngwakọta ndị ọkachamara (MoE)? Nkọwa | AI Understanding

Nkọwa

Ihe owuwu nke nwere subnetworks pụrụ iche ebe naanị ndị ọkachamara ahọpụtara na-agba ọsọ kwa ntinye.

Okwu ndị metụtara

Ọrụ ebe ihe nlere na-ekenye ntinye n'otu otu ma ọ bụ karịa akọwapụtara.

Nleba anya windo slide

Usoro nlebara anya ebe akara ngosi nke ọ bụla na-aga naanị na windo nwere oke nke akara dị nso iji belata mkpokọta.

Usoro nlebara anya

Akụkụ ihe atụ nke na-elekwasị anya na akụkụ dị mkpa nke ntinye mgbe ị na-emepụta mmepụta.

Chain-nke-Echere

Ụdị ntụgharị uche ebe ihe atụ AI na-emebi nsogbu n'ime usoro etiti.

Ihe owuwu ihe atụ multimodal nke na-amụta ihe nnochite anya n'etiti ederede na onyonyo.

Akụrụngwa nhazi achọrọ iji zụọ na ịgba ọsọ ụdị, a na-atụkarị ya na FLOPS ma ọ bụ awa GPU.

Mụtakwuo na ntuziaka efu anyị

Expert Parallelism for MoE Serving Mixture-of-Agents Aggregation Mixture of Depths Mixture of LoRA Experts

Hụkwa

Memory (Agent Memory)Model Card Machine Learning (ML)Model Drift Loss Function Model Quantization LoRA (Low-Rank Adaptation)Multimodal Model

Chọgharịa akwụkwọ nkọwa AI zuru ezu Nyochaa ntuziaka AI niile efu