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🌐 [i18n-KO] Translated main_classes/quantization.md to Korean #33959

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@fabxoe fabxoe commented Oct 4, 2024

What does this PR do?

Translated the main_classes/quantization.md file of the documentation to Korean.
Thank you in advance for your review.

Part of #20179

Before reviewing

  • Check for missing / redundant translations (번역 누락/중복 검사)
  • Grammar Check (맞춤법 검사)
  • Review or Add new terms to glossary (용어 확인 및 추가)
  • Check Inline TOC (e.g. [[lowercased-header]])
  • Check live-preview for gotchas (live-preview로 정상작동 확인)

Who can review? (Initial)

@chhaewxn, @ahnjj, @jun048098, @fabxoe, @nuatmochoi, @heuristicwave

Before submitting

  • This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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    here are tips on formatting docstrings.
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Who can review? (Final)


# 양자화[[quantization]]

Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. Transformers supports the AWQ and GPTQ quantization algorithms and it supports 8-bit and 4-bit quantization with bitsandbytes.
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Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. Transformers supports the AWQ and GPTQ quantization algorithms and it supports 8-bit and 4-bit quantization with bitsandbytes.

아래 번역 해두신것 같아 원문부분 삭제했습니다~!


Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. Transformers supports the AWQ and GPTQ quantization algorithms and it supports 8-bit and 4-bit quantization with bitsandbytes.

Quantization techniques that aren't supported in Transformers can be added with the [`HfQuantizer`] class.
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Quantization techniques that aren't supported in Transformers can be added with the [`HfQuantizer`] class.

아래 번역 해두신것 같아 원문부분 삭제했습니다~!


<Tip>

이 [양자화](../quantization) 가이드를 통해서 모델을 양자화하는 방법을 배울 수 있습니다.
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Suggested change
[양자화](../quantization) 가이드를 통해서 모델을 양자화하는 방법을 배울 수 있습니다.
모델을 양자화하는 방법은 이 [양자화](../quantization) 가이드를 통해 배울 수 있습니다.

좀더 가독성 있어보이게 바꾸었습니다!

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