Repository: huggingface/transformers
Pull request: 🌐 [i18n-KO] Translated quark.md to Korean

# What does this PR do?

Translated the quark.md file of the documentation to Korean.
Thank you in advance for your review.

Part of https://github.com/huggingface/transformers/issues/20179
## Before reviewing

  - [x]  Check for missing / redundant translations (번역 누락/중복 검사)
  - [x] Grammar Check (맞춤법 검사)
  - [x] Review or Add new terms to glossary (용어 확인 및 추가)
  - [x]  Check Inline TOC (e.g. [[lowercased-header]])
  - [ ]  Check live-preview for gotchas (live-preview로 정상작동 확인)

## Who can review? (Initial)

@4N3MONE, @harheem @jungnerd @yijun-lee 
## Before submitting

   - [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
   - [x]  Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
    Pull Request section?
   - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
    to it if that's the case.
   - [x]  Did you make sure to update the documentation with your changes? Here are the
    [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
    [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
   - [ ]  Did you write any new necessary tests?

## Who can review? (Final)

@stevhliu May you please review this PR?

Changed files:
filename: docs/source/ko/_toctree.yml
patch: @@ -157,6 +157,8 @@
     title: (번역중) VPTQ
   - local: quantization/quanto
     title: Quanto
+  - local: quantization/quark
+    title: Quark
   - local: quantization/eetq
     title: EETQ
   - local: in_translation

filename: docs/source/ko/quantization/quark.md
patch: @@ -0,0 +1,85 @@
+<!--Copyright 2025 Advanced Micro Devices, Inc. and 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.
+
+-->
+
+# Quark[[quark]]
+
+[Quark](https://quark.docs.amd.com/latest/)는 특정 데이터 타입, 알고리즘, 하드웨어에 구애받지 않도록 설계된 딥러닝 양자화 툴킷입니다. Quark에서는 다양한 전처리 전략, 알고리즘, 데이터 타입을 조합하여 사용할 수 있습니다.
+
+🤗 Transformers를 통해 통합된 PyTorch 지원은 주로 AMD CPU 및 GPU를 대상으로 하며, 주로 평가 목적으로 사용됩니다. 예를 들어, [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)를 🤗 Transformers 백엔드와 함께 사용하여 Quark로 양자화된 다양한 모델을 원활하게 평가할 수 있습니다.
+
+Quark에 관심이 있는 사용자는 [문서](https://quark.docs.amd.com/latest/)를 참고하여 모델 양자화를 시작하고 지원되는 오픈 소스 라이브러리에서 사용할 수 있습니다!
+
+Quark는 자체 체크포인트/[설정 포맷](https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV-Quark-test/blob/main/config.json#L26)를 가지고 있지만, 다른 양자화/런타임 구현체 ([AutoAWQ](https://huggingface.co/docs/transformers/quantization/awq), [네이티브 fp8](https://huggingface.co/docs/transformers/quantization/finegrained_fp8))와 호환되는 직렬화 레이아웃으로 모델을 생성하는 것도 지원합니다.
+
+Transformer에서 Quark 양자화 모델을 로드하려면 먼저 라이브러리를 설치해야 합니다:
+
+```bash
+pip install amd-quark
+```
+
+## 지원 매트릭스[[Support matrix]]
+
+Quark를 통해 양자화된 모델은 함께 조합할 수 있는 광범위한 기능을 지원합니다. 구성에 관계없이 모든 양자화된 모델은 `PretrainedModel.from_pretrained`를 통해 원활하게 다시 로드할 수 있습니다.
+
+아래 표는 Quark에서 지원하는 몇 가지 기능을 보여줍니다:
+
+| **기능**                        | **Quark에서 지원하는 항목**                                                                             |   |
+|---------------------------------|-----------------------------------------------------------------------------------------------------------|---|
+| 데이터 타입                     | int8, int4, int2, bfloat16, float16, fp8_e5m2, fp8_e4m3, fp6_e3m2, fp6_e2m3, fp4, OCP MX, MX6, MX9, bfp16 |   |
+| 양자화 전 모델 변환 | SmoothQuant, QuaRot, SpinQuant, AWQ                                                                       |   |
+| 양자화 알고리즘                 | GPTQ                                                                                                      |   |
+| 지원 연산자                     | ``nn.Linear``, ``nn.Conv2d``, ``nn.ConvTranspose2d``, ``nn.Embedding``, ``nn.EmbeddingBag``               |   |
+| 세분성(Granularity)             | per-tensor, per-channel, per-block, per-layer, per-layer type                                             |   |
+| KV 캐시                         | fp8                                                                                                       |   |
+| 활성화 캘리브레이션             | MinMax / Percentile / MSE                                                                                 |   |
+| 양자화 전략                     | weight-only, static, dynamic, with or without output quantization                                         |   |
+
+## Hugging Face Hub의 모델[[Models on Hugging Face Hub]]
+
+Quark 네이티브 직렬화를 사용하는 공개 모델은 https://huggingface.co/models?other=quark 에서 찾을 수 있습니다.
+
+Quark는 [`quant_method="fp8"`을 이용하는 모델](https://huggingface.co/models?other=fp8)과 [`quant_method="awq"`을 사용하는 모델](https://huggingface.co/models?other=awq)도 지원하지만, Transformers는 이러한 모델을 [AutoAWQ](https://huggingface.co/docs/transformers/quantization/awq)를 통해 불러오거나 
+[🤗 Transformers의 네이티브 fp8 지원](https://huggingface.co/docs/transformers/quantization/finegrained_fp8)을 사용합니다.
+
+## Transformers에서 Quark모델 사용하기[[Using Quark models in Transformers]]
+
+다음은 Transformers에서 Quark 모델을 불러오는 방법의 예시입니다:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "EmbeddedLLM/Llama-3.1-8B-Instruct-w_fp8_per_channel_sym"
+model = AutoModelForCausalLM.from_pretrained(model_id)
+model = model.to("cuda")
+
+print(model.model.layers[0].self_attn.q_proj)
+# QParamsLinear(
+#   (weight_quantizer): ScaledRealQuantizer()
+#   (input_quantizer): ScaledRealQuantizer()
+#   (output_quantizer): ScaledRealQuantizer()
+# )
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+inp = tokenizer("Where is a good place to cycle around Tokyo?", return_tensors="pt")
+inp = inp.to("cuda")
+
+res = model.generate(**inp, min_new_tokens=50, max_new_tokens=100)
+
+print(tokenizer.batch_decode(res)[0])
+# 
Where is a good place to cycle around Tokyo? There are several places in Tokyo that are suitable for cycling, depending on your skill level and interests. Here are a few suggestions:
+# 1. Yoyogi Park: This park is a popular spot for cycling and has a wide, flat path that's perfect for beginners. You can also visit the Meiji Shrine, a famous Shinto shrine located in the park.
+# 2. Imperial Palace East Garden: This beautiful garden has a large, flat path that's perfect for cycling. You can also visit the
+```