🌐 [i18n-KO] Translated eetq.md to Korean (#32352)

* docs: ko: quantization/eetq.md

* feat: nmt draft

* fix docs: ko: quantization/eetq.md

* fix docs: ko: quantization/eetq.md

* fix: resolve suggestions

Co-authored-by: Jiwook Han <33192762+mreraser@users.noreply.github.com>

* fix: resolve suggestions

* fix: resolve suggsetions

---------

Co-authored-by: Jiwook Han <33192762+mreraser@users.noreply.github.com>

docs/source/ko/_toctree.yml

@@ -151,8 +151,8 @@
     title: (번역중) AQLM
   - local: quantization/quanto
     title: Quanto
-  - local: in_translation
-    title: (번역중) EETQ
+  - local: quantization/eetq
+    title: EETQ
   - local: in_translation
     title: (번역중) HQQ
   - local: in_translation

docs/source/ko/quantization/eetq.md

@@ -0,0 +1,47 @@
+<!--Copyright 2024 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.
+
+-->
+
+# EETQ [[eetq]]
+
+[EETQ](https://github.com/NetEase-FuXi/EETQ) 라이브러리는 NVIDIA GPU에 대해 int8 채널별(per-channel) 가중치 전용 양자화(weight-only quantization)을 지원합니다. 고성능 GEMM 및 GEMV 커널은 FasterTransformer 및 TensorRT-LLM에서 가져왔습니다. 교정(calibration) 데이터셋이 필요 없으며, 모델을 사전에 양자화할 필요도 없습니다. 또한, 채널별 양자화(per-channel quantization) 덕분에 정확도 저하가 미미합니다.
+
+[릴리스 페이지](https://github.com/NetEase-FuXi/EETQ/releases)에서 eetq를 설치했는지 확인하세요.
+```
+pip install --no-cache-dir https://github.com/NetEase-FuXi/EETQ/releases/download/v1.0.0/EETQ-1.0.0+cu121+torch2.1.2-cp310-cp310-linux_x86_64.whl
+```
+또는 소스 코드 https://github.com/NetEase-FuXi/EETQ 에서 설치할 수 있습니다. EETQ는 CUDA 기능이 8.9 이하이고 7.0 이상이어야 합니다.
+```
+git clone https://github.com/NetEase-FuXi/EETQ.git
+cd EETQ/
+git submodule update --init --recursive
+pip install .
+```
+
+비양자화 모델은 "from_pretrained"를 통해 양자화할 수 있습니다.
+```py
+from transformers import AutoModelForCausalLM, EetqConfig
+path = "/path/to/model".
+quantization_config = EetqConfig("int8")
+model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", quantization_config=quantization_config)
+```
+
+양자화된 모델은 "save_pretrained"를 통해 저장할 수 있으며, "from_pretrained"를 통해 다시 사용할 수 있습니다.
+
+```py
+quant_path = "/path/to/save/quantized/model"
+model.save_pretrained(quant_path)
+model = AutoModelForCausalLM.from_pretrained(quant_path, device_map="auto")
+```