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

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# What does this PR do?

Translated the `jamba.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]]`)
- [x] Check live-preview for gotchas (live-preview로 정상작동 확인)

## Who can review? (Initial)

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May you please review this PR?
@jungnerd, @luckyvickyricky, @chelsseeey, @amo33, @maximizemaxwell, @D15M4S
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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
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      to it if that's the case.
- [ ] 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)

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@stevhliu May you please review this PR?

Changed files:
filename: docs/source/ko/_toctree.yml
patch: @@ -597,7 +597,7 @@
         title: HGNet-V2
       - local: in_translation
         title: I-BERT
-      - local: in_translation
+      - local: model_doc/jamba
         title: Jamba
       - local: in_translation
         title: JetMoe
@@ -1241,4 +1241,4 @@
     - local: in_translation
       title: (번역중)Environment Variables
     title: Reference
-  title: API
+  title: API
\ No newline at end of file

filename: docs/source/ko/model_doc/jamba.md
patch: @@ -0,0 +1,158 @@
+<!--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.
+
+-->
+
+<div style="float: right;">
+  <div class="flex flex-wrap space-x-1">
+    <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+    <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+    <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+  </div>
+</div>
+
+# Jamba[[jamba]]
+
+[Jamba](https://huggingface.co/papers/2403.19887)는 Transformer와 Mamba 기반의 하이브리드 전문가 혼합(MoE) 언어 모델로, 총 매개변수 수는 52B에서 398B까지 다양합니다. 이 모델은 Transformer 모델의 성능과 Mamba와 같은 상태 공간 모델의 효율성 및 긴 컨텍스트 처리 능력(256K 토큰)을 모두 활용하는 것을 목표로 합니다.
+
+Jamba의 아키텍처는 블록과 레이어 기반 구조를 사용하여 Transformer와 Mamba 아키텍처를 통합할 수 있도록 설계되었습니다. 각 Jamba 블록은 어텐션 레이어 또는 Mamba 레이어 중 하나와 그 뒤를 잇는 다층 퍼셉트론(MLP)으로 구성되어 있습니다. Transformer 레이어는 8개의 레이어 중 하나의 비율로 주기적으로 배치됩니다. 또한 모델 용량을 확장하기 위해 MoE 레이어가 혼합되어 있습니다.
+
+모든 원본 Jamba 체크포인트는 [AI21](https://huggingface.co/ai21labs) 조직에서 확인할 수 있습니다.
+
+> [!TIP]
+> 오른쪽 사이드바에 있는 Jamba 모델을 누르면 다양한 언어 작업에 Jamba를 적용하는 예제를 더 확인할 수 있습니다.
+
+아래 예제는 [`Pipeline`]과 [`AutoModel`], 그리고 커맨드라인을 통해 텍스트를 생성하는 방법을 보여줍니다.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```py
+# 최적화된 Mamba 구현 설치
+# !pip install mamba-ssm causal-conv1d>=1.2.0
+import torch
+from transformers import pipeline
+
+pipeline = pipeline(
+    task="text-generation",
+    model="ai21labs/AI21-Jamba-Mini-1.6",
+    torch_dtype=torch.float16,
+    device=0
+)
+pipeline("Plants create energy through a process known as")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "ai21labs/AI21-Jamba-Large-1.6",
+)
+model = AutoModelForCausalLM.from_pretrained(
+    "ai21labs/AI21-Jamba-Large-1.6",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to("cuda")
+
+output = model.generate(**input_ids, cache_implementation="static")
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+</hfoption>
+<hfoption id="transformers CLI">
+
+```bash
+echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model ai21labs/AI21-Jamba-Mini-1.6 --device 0
+```
+
+</hfoption>
+</hfoptions>
+
+양자화는 가중치를 더 낮은 정밀도로 표현하여 대규모 모델의 메모리 부담을 줄여줍니다. 사용할 수 있는 다양한 양자화 백엔드에 대해서는 [Quantization](../quantization/overview)를 참고하세요.
+
+아래 예제는 [bitsandbytes](../quantization/bitsandbytes)를 사용하여 가중치만 8비트로 양자화하는 방법을 보여줍니다.
+
+```py
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(load_in_8bit=True,
+                                         llm_int8_skip_modules=["mamba"])
+
+# 모델을 8개의 GPU에 고르게 분산시키기 위한 디바이스 맵
+device_map = {'model.embed_tokens': 0, 'model.layers.0': 0, 'model.layers.1': 0, 'model.layers.2': 0, 'model.layers.3': 0, 'model.layers.4': 0, 'model.layers.5': 0, 'model.layers.6': 0, 'model.layers.7': 0, 'model.layers.8': 0, 'model.layers.9': 1, 'model.layers.10': 1, 'model.layers.11': 1, 'model.layers.12': 1, 'model.layers.13': 1, 'model.layers.14': 1, 'model.layers.15': 1, 'model.layers.16': 1, 'model.layers.17': 1, 'model.layers.18': 2, 'model.layers.19': 2, 'model.layers.20': 2, 'model.layers.21': 2, 'model.layers.22': 2, 'model.layers.23': 2, 'model.layers.24': 2, 'model.layers.25': 2, 'model.layers.26': 2, 'model.layers.27': 3, 'model.layers.28': 3, 'model.layers.29': 3, 'model.layers.30': 3, 'model.layers.31': 3, 'model.layers.32': 3, 'model.layers.33': 3, 'model.layers.34': 3, 'model.layers.35': 3, 'model.layers.36': 4, 'model.layers.37': 4, 'model.layers.38': 4, 'model.layers.39': 4, 'model.layers.40': 4, 'model.layers.41': 4, 'model.layers.42': 4, 'model.layers.43': 4, 'model.layers.44': 4, 'model.layers.45': 5, 'model.layers.46': 5, 'model.layers.47': 5, 'model.layers.48': 5, 'model.layers.49': 5, 'model.layers.50': 5, 'model.layers.51': 5, 'model.layers.52': 5, 'model.layers.53': 5, 'model.layers.54': 6, 'model.layers.55': 6, 'model.layers.56': 6, 'model.layers.57': 6, 'model.layers.58': 6, 'model.layers.59': 6, 'model.layers.60': 6, 'model.layers.61': 6, 'model.layers.62': 6, 'model.layers.63': 7, 'model.layers.64': 7, 'model.layers.65': 7, 'model.layers.66': 7, 'model.layers.67': 7, 'model.layers.68': 7, 'model.layers.69': 7, 'model.layers.70': 7, 'model.layers.71': 7, 'model.final_layernorm': 7, 'lm_head': 7}
+model = AutoModelForCausalLM.from_pretrained("ai21labs/AI21-Jamba-Large-1.6",
+                                             torch_dtype=torch.bfloat16,
+                    attn_implementation="flash_attention_2",
+                                             quantization_config=quantization_config,
+                                             device_map=device_map)
+
+tokenizer = AutoTokenizer.from_pretrained("ai21labs/AI21-Jamba-Large-1.6")
+
+messages = [
+   {"role": "system", "content": "You are an ancient oracle who speaks in cryptic but wise phrases, always hinting at deeper meanings."},
+   {"role": "user", "content": "Hello!"},
+]
+
+input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors='pt').to(model.device)
+
+outputs = model.generate(input_ids, max_new_tokens=216)
+
+# 출력 디코딩
+conversation = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# 어시스턴트의 응답만 추출
+assistant_response = conversation.split(messages[-1]['content'])[1].strip()
+print(assistant_response)
+# 출력: Seek and you shall find. The path is winding, but the journey is enlightening. What wisdom do you seek from the ancient echoes?
+```
+
+## 참고[[notes]]
+
+- 모델 성능 저하를 방지하기 위해 Mamba 블록은 양자화하지 마세요.
+- 최적화된 Mamba 커널 없이 Mamba를 사용하면 지연 시간이 크게 증가하므로 권장되지 않습니다. 그래도 커널 없이 Mamba를 사용하고자 한다면 [`~AutoModel.from_pretrained`]에서 `use_mamba_kernels=False`로 설정하세요.
+
+  ```py
+  import torch
+  from transformers import AutoModelForCausalLM
+  model = AutoModelForCausalLM.from_pretrained("ai21labs/AI21-Jamba-1.5-Large",
+                                               use_mamba_kernels=False)
+  ```
+
+## JambaConfig[[transformers.JambaConfig]]
+
+[[autodoc]] JambaConfig
+
+
+## JambaModel[[transformers.JambaModel]]
+
+[[autodoc]] JambaModel
+    - forward
+
+
+## JambaForCausalLM[[transformers.JambaForCausalLM]]
+
+[[autodoc]] JambaForCausalLM
+    - forward
+
+
+## JambaForSequenceClassification[[transformers.JambaForSequenceClassification]]
+
+[[autodoc]] transformers.JambaForSequenceClassification
+    - forward