量化测验

当您参加测验时，就会出现答案和解释。

1. 在以权重为中心的量化方案中，什么表示属性发生了变化？
   • The mandatory number of network layers
   • The identity of every original training document
   • The numerical levels or precision used for selected weights
   • Only the displayed model title
2. 忽略元数据和其他工作内存，当将相同的权重从 16 位移动到 4 位时，原始打包权重位的剩余部分是多少？
   • The same amount
   • Twice as much
   • One-hundredth in every case
   • One-quarter
3. 激进量化的主要缺点是什么？
   • 舍入误差会降低模型的准确性
   • 该模型无法再下载
   • 它永久删除训练数据
   • 它总是使模型变慢
4. 为什么引用的 GPTQ 和 AWQ 方法要检查校准输入？
   • To guarantee identical performance on every future input
   • To guide their respective weight-quantization choices and error management
   • To translate the vocabulary into another language
   • To add a required number of new transformer layers
5. 为什么必须测量运行时速度而不是仅从较低权重位宽推断？
   • A bitwidth label guarantees an identical speedup on every device
   • All quantized values remove the need for computation
   • Every compressed model must be slower by definition
   • Kernels, hardware, and conversion overhead also affect runtime
6. What does activation-aware mean in the cited AWQ weight-quantization method?
   • Every activation must be stored at the same low bitwidth as every weight
   • The weights are never quantized
   • Activation observations help choose salient weight scaling
   • The method uses only parameter names without input observations
7. Using the stated reconstruction rule scale times code minus zero point with subtraction inside, what value comes from code six, scale 0.5, and zero point two?
   • 四
   • 两个
   • 三
   • 六
8. How does quantization-aware training differ in timing from post-training quantization?
   • Training incorporates quantization effects so learned values can adapt
   • It is defined as changing only a fitted model after every training stage ends
   • It forbids all training examples
   • It guarantees no rounding or approximation error in any representation