quiz bu lëkkale ak tegtal · Diggante Niveau

Laajteb EleutherAI

Xoolaat xeetu làkk yu ubbeeku yu EleutherAI, setu done Pile, jumtukaayi jàngat, ak tànneefi architecture yu am solo yu melni RoPE.

Yooni njiit yu jëm ciEleuteray
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How did EleutherAI first bring together its research community?

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  1. How did EleutherAI first bring together its research community?
    • As a Discord server for discussing GPT-3 and open language modeling
    • Bi nga xamee ni ab sosete buy tàmbali ci Silicon Valley
    • Laboratoire gëstu bu nguur gi
    • As a university’s official AI department
  2. Luy 'Pile' bi?
    • Produit chatbot buñu moomeel
    • ab done bu ubbeeku bu 825GB ngir tàggat ay xeetu làkk
    • Benn xeetu lalu reso neuronal
    • Platform ordinatëru niir bu EleutherAI
  3. Ci yii, yan ci ñoom mooy xeetu làkku EleutherAI?
    • BERT-Yaatu
    • Llama-2
    • GPT-NeoX-20B
    • PALM
  4. Lan lañuy jëfandikoo ngir jàngat LM?
    • Defar ay nataal ci mbind
    • kompresse diisaayu model
    • Royuwaayi dalal ni API web
    • Benchmarking xeetu làkk ci liggéey yu bari
  5. Which organizations are named in EleutherAI’s retrospective as research supporters?
    • CoreWeave and Google TPU Research Cloud
    • Nvidia and the National Science Foundation only
    • The U.S. Department of Energy and NASA
    • Amazon and the European Space Agency
  6. Ban coppite ci mbootaay la EleutherAI amal ci 2023?
    • OpenAI moo ko jëndoon
    • Ñu ngi ko formalise ni daara buy def gëstu buy def te Yàlla tax
    • Nekk na fond de couverture buy wut xaalis
    • Dafa tëju lëmm
  7. Which positional method does the GPT-NeoX-20B paper describe using instead of learned absolute position embeddings?
    • A separate learned embedding for every document
    • A convolutional image-position map
    • Rotary Positional Embeddings (RoPE)
    • A random position permutation at each layer
  8. What architectural arrangement is highlighted as a GPT-NeoX-20B difference from GPT-3?
    • The model replaces its Transformer with a decision tree
    • Every layer removes the attention mechanism
    • It uses only an encoder and no autoregressive decoder
    • Attention and feed-forward layers can be computed in parallel