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量化：构建更小、更快的模型

使用 int8 推理压缩权重

量化：构建更小、更快的模型 是 CoddyKit 上的免费 Deep Learning Academy 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Deep Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Deep Learning Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Smaller Weights, Faster Models

Big models are slow and heavy to serve. Quantization shrinks them by storing numbers with fewer bits, so they run faster and lighter. 📉

Float32 vs Int8

Models usually store weights as 32-bit floats. Quantization converts them to 8-bit integers, cutting size by roughly four times.

Why Int8 Runs Faster

Integer math is cheaper than floating-point on most hardware, so int8 inference uses less memory bandwidth and finishes sooner.

Mapping Floats to Integers

A scale and zero-point map each float range onto integers. They let the model recover an approximate float value when computing.

Expect a Tiny Accuracy Cost

Fewer bits means some precision is lost, so accuracy may dip slightly. For most models the drop is small and well worth the speed.

Dynamic Quantization: The Easy Win

Dynamic quantization is the simplest path. It quantizes weights ahead of time and activations on the fly, ideal for linear and RNN layers.

import torch
q = torch.quantization.quantize_dynamic(
  model, {torch.nn.Linear}, dtype=torch.qint8)

Static Quantization: Calibrate First

Static quantization also quantizes activations ahead of time. You feed it sample data to calibrate ranges, gaining more speed on CPUs.

Quantization-Aware Training

For the best accuracy, quantization-aware training simulates int8 during training so the model learns to tolerate the lower precision.

Measure the Size Win

After quantizing, save the model and compare file sizes. An int8 version is typically about a quarter of the float32 original. 💾

torch.save(q.state_dict(), 'model_int8.pt')

Always Re-Test Accuracy

Run your validation set on the quantized model and confirm accuracy is still acceptable before you deploy it to real users.

Quantization Shines on CPU and Edge

Quantization pays off most on CPUs, phones, and edge devices where memory is tight and integer math is well supported. 📱

Quick Check

You want the quickest quantization with no calibration step. Which fits?

Recap: Lighter and Faster

You shrank a model with quantization, traded float32 for int8, picked dynamic, static, or aware training, and re-checked accuracy. 🎉

常见问题解答

「量化：构建更小、更快的模型」课时是免费的吗？

是的 — 「量化：构建更小、更快的模型」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Deep Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Deep Learning Academy 课程共包含 4 节课。

「量化：构建更小、更快的模型」这节课中我会学到什么？

使用 int8 推理压缩权重你通过在浏览器中直接运行的动手代码来练习 Deep Learning Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Deep Learning Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Deep Learning Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「量化：构建更小、更快的模型」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Deep Learning Academy 课中编写并运行代码吗？

能。每节 Deep Learning Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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