引导式测验 · 中等 等级

小语言模型测验

比较紧凑的模型资源，包括精选数据、蒸馏、修剪、量化权重存储、KV 缓存和原始 Phi-3 训练规模。

相关引导路径小语言模型
问题 1 的 8

什么资源目标推动了紧凑语言模型实现？

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  1. 什么资源目标推动了紧凑语言模型实现？
    • Guaranteeing it beats every larger model on every task
    • Reducing resource demands for a chosen task and device setup
    • Eliminating the need for all training data
    • Proving every phone runs every checkpoint identically
  2. 所引用的 Phi 研究除了模型大小外，还研究了哪些训练因素？
    • Curated, filtered or synthetic training-data choices
    • Only random unfiltered data with no objective
    • Removal of all model parameters
    • A requirement to train only at night
  3. 教师/学生知识蒸馏训练学生从什么中学习？
    • Only a new image-file extension
    • A guarantee of every possible teacher capability
    • The teacher’s outputs or distributions under a stated objective
    • Only a hardware-temperature label
  4. 权重量化在此处描述的存储设置中改变了什么？
    • Necessarily the number of model parameters
    • The language of every output sentence
    • Only the order of filenames
    • The numerical representation and bits used for weights
  5. 哪种压缩操作会移除或抑制选定的网络连接？
    • 修剪
    • Only changing a prompt’s font
    • Only enlarging the vocabulary
    • Only retaining every weight at its original value
  6. What work does a key-value attention cache avoid repeating during autoregressive generation?
    • Every possible future training operation
    • All text-tokenization work for every application
    • Recomputing prior keys and values for subsequent token processing
    • The need to retain any state at all
  7. Ignoring all overhead, how much raw storage is needed for 3.8 billion scalar weights packed at 4 bits each?
    • 3.8 billion bytes
    • 15.2 billion bytes
    • 0.475 billion bytes
    • 1.9 billion bytes
  8. How many training tokens does the original Phi-3 report state for phi-3-mini?
    • 3.8 billion tokens
    • 3.3 trillion tokens
    • Only 3.3 million tokens
    • No training-token count was stated