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

* docs: ko: main_classes/quantization.md

* feat: nmt draft

* fix: resolve suggestions

Co-authored-by: Ahnjj_DEV <ahnjj.dev@gmail.com>

* fix: resolve suggestions

Co-authored-by: Ahnjj_DEV <ahnjj.dev@gmail.com>

* fix: resolve suggestions

---------

Co-authored-by: Ahnjj_DEV <ahnjj.dev@gmail.com>

docs/source/ko/_toctree.yml

@@ -298,8 +298,8 @@
       title: (번역중) Pipelines
     - local: in_translation
       title: (번역중) Processors
-    - local: in_translation
+    - local: main_classes/quantization
-      title: (번역중) Quantization
+      title: 양자화
     - local: in_translation
       title: (번역중) Tokenizer
     - local: main_classes/trainer

docs/source/ko/main_classes/quantization.md

@@ -0,0 +1,71 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# 양자화[[quantization]]
+
+
+
+양자화 기법은 가중치와 활성화를 8비트 정수(int8)와 같은 더 낮은 정밀도의 데이터 타입으로 표현함으로써 메모리와 계산 비용을 줄입니다. 이를 통해 일반적으로는 메모리에 올릴 수 없는 더 큰 모델을 로드할 수 있고, 추론 속도를 높일 수 있습니다. Transformers는 AWQ와 GPTQ 양자화 알고리즘을 지원하며, bitsandbytes를 통해 8비트와 4비트 양자화를 지원합니다.
+Transformers에서 지원되지 않는 양자화 기법들은 [`HfQuantizer`] 클래스를 통해 추가될 수 있습니다.
+
+<Tip>
+
+모델을 양자화하는 방법은 이 [양자화](../quantization) 가이드를 통해 배울 수 있습니다.
+
+</Tip>
+
+## QuantoConfig[[transformers.QuantoConfig]]
+
+[[autodoc]] QuantoConfig
+
+## AqlmConfig[[transformers.AqlmConfig]]
+
+[[autodoc]] AqlmConfig
+
+## AwqConfig[[transformers.AwqConfig]]
+
+[[autodoc]] AwqConfig
+
+## EetqConfig[[transformers.EetqConfig]]
+[[autodoc]] EetqConfig
+
+## GPTQConfig[[transformers.GPTQConfig]]
+
+[[autodoc]] GPTQConfig
+
+## BitsAndBytesConfig[[#transformers.BitsAndBytesConfig]]
+
+[[autodoc]] BitsAndBytesConfig
+
+## HfQuantizer[[transformers.quantizers.HfQuantizer]]
+
+[[autodoc]] quantizers.base.HfQuantizer
+
+## HqqConfig[[transformers.HqqConfig]]
+
+[[autodoc]] HqqConfig
+
+## FbgemmFp8Config[[transformers.FbgemmFp8Config]]
+
+[[autodoc]] FbgemmFp8Config
+
+## CompressedTensorsConfig[[transformers.CompressedTensorsConfig]]
+
+[[autodoc]] CompressedTensorsConfig
+
+## TorchAoConfig[[transformers.TorchAoConfig]]
+
+[[autodoc]] TorchAoConfig