Guide-yakabatana mibvunzo · Pakati Nhanho

Poolside AI Code Generation Quiz

Wongorora mhando dzepoolside kodhi, mhinduro dzekuita, kutumirwa kwebhizinesi, chiyero chemodhiyo, uye mabhenji-chaiwo zvichemo. Tarisa zvinyorwa uye chiyero.

Nzira dzekutungamira dzinoenderanaPoolside AI Code Generation
Mubvunzo 1 ye 8

Ndeipi nzira yekudzidzisa yePoolside inozivikanwa?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Ndeipi nzira yekudzidzisa yePoolside inozivikanwa?
    • Kusimbisa Kudzidza kubva kuCode Execution Feedback (RLCEF)
    • Kwakachena kusatariswa pretraining paGitHub chete
    • Kusimbisa kwevanhu kudzidza kubva ku chat ratings
    • Genetic programming isina neural network
  2. What do Poolside’s Laguna models target?
    • Image segmentation only
    • Agentic software development and code-generation tasks
    • Consumer finance scoring
    • Audio transcription only
  3. Chii chinosimbisa Poolside kune vatengi vemabhizinesi?
    • Yemahara yeruzhinji API kuwana kune chero munhu
    • Chete chevatengi nharembozha
    • Yakavanzika uye pane-nzvimbo yekuendesa saka kodhi inogara mumba
    • Kutengesa data yevashandisi kuvashambadziri
  4. What should a reader use to assess Poolside’s reported benchmark scores?
    • A general claim that one model is best
    • Parameter count alone
    • A social-media post without methodology
    • The model/version, benchmark setup, and evaluation source stated with the result
  5. Sei mhinduro yekodhi yakakosha pakudzidzisa modhi yekodhi?
    • Kururama kunogona kutariswa otomatiki, kuunza scalable mibairo masaini pasina mazita emunhu
    • Inoita kuti modhi imhanye nekukurumidza pakufungidzira
    • Inobvisa kudiwa kwechero maGPU
    • Inovimbisa iyo modhi haina kumbokanganisa
  6. What context lengths does the Laguna M.1/XS.2 technical report describe for continued pre-training?
    • 2K and 4K tokens only
    • 32K and 128K token sequence lengths
    • 1M and 2M tokens in every released model
    • 30 and 60 seconds of generated code
  7. Which framework does Poolside cite for the Laguna M.1/XS.2 agentic benchmark evaluations?
    • GLUE
    • ImageNet
    • The Laude Institute’s Harbor Framework
    • Common Crawl
  8. What should an enterprise review before deploying a code model?
    • Zita remuenzaniso chete
    • Whether the UI uses a dark theme
    • A benchmark score alone
    • Data handling, access controls, model terms, and evaluation on its own repositories