Тест, свързан с ръководство · Среден Ниво

Течен AI и тест за модели на течна основа

Свържете оригиналното непрекъснато изследване на Liquid AI с остарялата хибридна архитектура LFM2 и констатациите на доклада с обхват за изводи по ръба.

Свързани ръководни пътекиМодели на Liquid AI и Liquid Foundation
Въпрос 1 на 8

What does “LFM” stand for in the LFM2 technical report?

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Отговорите и обясненията се появяват, докато отговаряте на теста.

  1. What does “LFM” stand for in the LFM2 technical report?
    • Liquid Foundation Model
    • Latent Feature Mapper
    • Long-Form Memory
    • Linear Forecasting Module
  2. According to the LFM2 technical report, what architecture did its hybrid backbone combine?
    • Only a dense layer with no sequence processing
    • Gated short convolutions with a small number of grouped-query attention blocks
    • A vision-only encoder and a decision tree
    • A single unchanging recurrent state with no attention
  3. What is the LFM2-8.3B mixture-of-experts model’s active parameter count?
    • All 8.3 billion parameters are active for every token
    • About 350 million total parameters
    • About 1.5 billion active parameters
    • The report gives no active parameter count
  4. What context length does the LFM2 report list for the model family?
    • 2K tokens only
    • 128K tokens for every variant
    • The report lists only audio duration
    • 32K токена
  5. What deployment setting does the LFM2 report explicitly optimize its architecture for?
    • On-device inference under latency, memory, and energy constraints, including phone- and laptop-class hardware
    • Guaranteed private operation on every car and phone
    • Cloud-only training on large GPU clusters
    • A fixed constant-memory budget independent of context and model size
  6. What CPU speed comparison did the November 2025 LFM2 report make against similarly sized models?
    • Exactly 2× higher accuracy on every benchmark
    • Up to 2× faster prefill and decode
    • A fixed 32× reduction in memory for every device
    • A guaranteed 2× improvement over every Transformer workload
  7. What constraint guided the hardware-in-the-loop architecture search described in the LFM2 report?
    • The need to maximize image resolution only
    • A requirement to train exclusively on one dataset
    • Edge-device latency and memory limits
    • The number of labels available for medical diagnosis
  8. What is LFM2-ColBERT designed to provide?
    • An audio-only speech synthesizer
    • A general-purpose image decoder
    • A controller for training liquid time-constant networks
    • A low-latency encoder for queries and documents used in retrieval