Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Yi Models by 01.AI Kedis

Dib u eeg asalka qoyska nooca Yi, diiradda laba-luqadlaha, naqshadaynta-kaliya, kala duwanaanshaha macnaha guud, iyo waxbarashada ku jihaysan tababarka dambe.

Wadooyinka haga ee la xidhiidhaMuuqaalada Yi ee 01 AI
Su'aal 1 ee 8

Yaa aasaasay 01.AI, shirkadda ka dambeysa moodooyinka Yi?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Yaa aasaasay 01.AI, shirkadda ka dambeysa moodooyinka Yi?
    • Kai-Fu Lee
    • Robin Li
    • Jack Ma
    • Andrew Ng
  2. Maxay ahaayeen labada nooc ee saldhigga Yi ee ugu horreeyay ee la sii daayay?
    • Yi-1 iyo Yi-2
    • Yi-6B iyo Yi-34B
    • Yi-100 iyo Yi-200
    • Yi-Mini iyo Yi-Max
  3. Awood noocee ah ayaa moodooyinka Yi si gaar ah loogu yaqaanaa?
    • Soo saarida sawirada kaliya
    • Ku shaqeeya iyada oo aan wax GPU ah
    • Waxqabad xooggan oo labada luqadood ku hadla ee Shiine-Ingiriisi
    • Turjumaada kaliya ee codka lagu hadlo
  4. Waa maxay nasabka dhismaha aasaasiga ah ee moodooyinka Yi raacaan?
    • Naqshad LSTM ah oo soo noqnoqota
    • Qaab fidin
    • Qaabka codeeyaha saafiga ah sida BERT
    • Beddeliyaha-qaabka Llama-kaliya
  5. Qiyaastii intee in le'eg ayay tahay daaqadda macnaha guud ee kala duwanaanshaha macnaha-dheer ee Yi?
    • 200k calaamadood
    • 4K calaamado
    • 32K calaamadood
    • 1 milyan oo calaamad
  6. What did 01.AI’s Yi technical report identify as an important driver of model performance?
    • Katagitaanka AI gebi ahaanba
    • Data quality resulting from data-engineering efforts
    • Kaliya dhis chips hardware
    • Ku biirista OpenAI
  7. What does supervised fine-tuning plus reinforcement learning from human feedback aim to improve in Yi chat models?
    • Compressing retinal images
    • Changing the model into an encoder-only network
    • Following instructions in conversational use
    • Removing all text-generation ability
  8. What data strategy did 01.AI emphasize for the Yi models?
    • Training exclusively on synthetic audio
    • Avoiding all data filtering
    • Using only programming-language source code
    • Careful data quality and curation rather than relying only on sheer scale