Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Kediska Models Zhipu GLM

Waxay dabooshaa magaca GLM iyo ujeedada tababarka, khadka ChatGLM, siidaynta moodeelka gaarka ah ee hadda, iyo fasiraadda taxaddarka leh ee cusbooneysiinta shirkadda.

Wadooyinka haga ee la xidhiidhaMoodooyinka Zhipu Glm
Su'aal 1 ee 8

Muxuu GLM uga taagan yahay moodooyinka Zhipu?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Muxuu GLM uga taagan yahay moodooyinka Zhipu?
    • Qaabka Guud ee Luuqadda
    • Mashiinka Waxbarashada Caalamiga ah
    • Qaabka Lakabka Garaafka
    • Module macquulka ah ee abuurista
  2. Jaamacdee Zhipu AI aad ugu dhow?
    • Jaamacadda Peking
    • Jaamacadda Tsinghua
    • MIT
    • Jaamacadda Fudan
  3. What did the ChatGLM-6B release make available to developers?
    • A video-only diffusion model
    • A proprietary API with no released model
    • A bilingual Chinese-English chatbot model with deployment options for local hardware
    • A database for image annotations
  4. What does CogVideoX generate from a text prompt?
    • A protein structure
    • A transaction graph
    • A speech transcript only
    • A video clip
  5. Sidee buu u shaqeeyaa ujeedada tababarka asalka ah ee GLM?
    • Waxay qarinaysaa taako qoraal ah waxayna si toos ah u buuxinaysaa meelaha bannaan
    • Keliya waxay saadaalisaa erayga soo socda
    • Waxay ururinaysaa ereyo aan lahayn summad
    • Waxay qiimaynaysaa natiijooyinka raadinta
  6. What is a stable technical question to ask about a specific Zhipu GLM release?
    • Whether all GLM releases have identical limits
    • Which capabilities, license, and evaluation results are documented for that exact version
    • Whether the company name predicts model quality
    • Whether a single benchmark proves general superiority
  7. What deployment option does the ChatGLM-6B repository document for reducing memory requirements?
    • Increasing the model to 70 billion parameters
    • Ka saarida dhammaan lakabyada dareenka
    • Quantized inference configurations such as 4-bit loading
    • Training a new tokenizer at each request
  8. How does the original GLM paper describe positional information for infilling spans?
    • It uses no position information
    • It assigns each document one global position
    • It treats masked spans as image patches
    • It uses two-dimensional positions to represent text position and the infilling span