# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2023, Xorbits Inc.
# This file is distributed under the same license as the Xinference package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2023.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version:  Xinference\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2026-07-17 17:20+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language: ko\n"
"Language-Team: ko <LL@li.org>\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Generated-By: Babel 2.17.0\n"

#: ../../source/user_guide/backends.rst:5
msgid "Backends"
msgstr "추론 엔진"

#: ../../source/user_guide/backends.rst:7
msgid ""
"Xinference supports multiple backends for different models. After the "
"user specifies the model, xinference will automatically select the "
"appropriate backend."
msgstr ""
"Xinference는 모델별로 다른 추론 엔진을 지원합니다. 사용자가 모델을 선택하면 Xinference가 자동으로 적합한 엔진을 "
"선택합니다."

#: ../../source/user_guide/backends.rst:11
msgid "llama.cpp"
msgstr "llama.cpp"

#: ../../source/user_guide/backends.rst:13
msgid ""
"Xinference now supports `xllamacpp "
"<https://github.com/xorbitsai/xllamacpp>`_ which developed by Xinference "
"team to run llama.cpp backend. `llama.cpp` is developed based on the "
"tensor library `ggml`, supporting inference of the LLaMA series models "
"and their variants."
msgstr ""
"Xinference는 현재 Xinference 팀이 개발한 `xllamacpp "
"<https://github.com/xorbitsai/xllamacpp>`_ 를 llama.cpp 백엔드로 지원합니다. "
"`llama.cpp` 는 텐서 라이브러리 `ggml` 을 기반으로 개발되었으며, LLaMA 시리즈 모델과 그 변형들의 추론을 "
"지원합니다."

#: ../../source/user_guide/backends.rst:20
msgid ""
"Since Xinference v1.5.0, ``xllamacpp`` becomes default option for "
"llama.cpp, and ``llama-cpp-python`` is deprecated. Since Xinference "
"v1.6.0, ``llama-cpp-python`` has been removed."
msgstr ""
"Xinference v1.5.0부터 ``xllamacpp`` 는 llama.cpp의 기본 옵션이 되었고, ``llama-cpp-"
"python`` 은 폐기되었습니다. Xinference v1.6.0부터 ``llama-cpp-python`` 은 제거되었습니다."

#: ../../source/user_guide/backends.rst:25
msgid "Automatic GPU wheel selection"
msgstr "GPU wheel 자동 선택"

#: ../../source/user_guide/backends.rst:29
msgid ""
"When per-model virtual environments are enabled and a usable NVIDIA GPU "
"is present, Xinference automatically installs the ``xllamacpp`` wheel "
"matching the detected CUDA line. CUDA 12.8 and later in the 12.x line use"
" the ``cu128`` wheel index, while CUDA 13.x uses the ``cu132`` wheel "
"index. CPU-only hosts and unsupported CUDA versions keep the default CPU "
"wheel from PyPI. No separate ``xllamacpp`` GPU installation command is "
"required for this launch path."
msgstr "모델별 가상 환경이 활성화되어 있고 사용 가능한 NVIDIA GPU가 있으면 Xinference는 감지된 CUDA 계열에 맞는 ``xllamacpp`` wheel을 자동으로 설치합니다. CUDA 12.x의 12.8 이상은 ``cu128`` wheel 인덱스를 사용하고 CUDA 13.x는 ``cu132`` wheel 인덱스를 사용합니다. CPU 전용 호스트와 지원되지 않는 CUDA 버전은 PyPI의 기본 CPU wheel을 계속 사용합니다. 이 실행 경로에서는 별도의 ``xllamacpp`` GPU 설치 명령이 필요하지 않습니다."

#: ../../source/user_guide/backends.rst:37
msgid ""
"For all configurable llama.cpp parameters, please refer to the definition"
" of the ``common_params`` structure in ``llama.cpp`` `common.h "
"<https://github.com/ggml-org/llama.cpp/blob/master/common/common.h>`_"
msgstr ""
"``llama.cpp``의 `common.h <https://github.com/ggml-"
"org/llama.cpp/blob/master/common/common.h>`_ 에 있는 ``common_params`` 구조체 "
"정의를 참고하여 파라미터를 설정하십시오."

#: ../../source/user_guide/backends.rst:39
msgid ""
"There may be some nested parameters. For example, ``sampling.top_k``. "
"Just use the ``.`` to separate nested parameters."
msgstr ""
"중첩된 여러 층의 매개변수가 있을 수 있습니다. 예를 들어, ``sampling.top_k`` 와 같습니다. 중첩된 매개변수를 "
"구분할 때는 ``.`` 을 사용하세요."

#: ../../source/user_guide/backends.rst:41
msgid "Here is an example of setting nested sampling parameters in WebUI:"
msgstr "다음은 WebUI에서 중첩 샘플링 매개변수를 설정하는 예시입니다:"

#: ../../source/user_guide/backends.rst:48
msgid "Auto NGL"
msgstr "자동 NGL"

#: ../../source/user_guide/backends.rst:50
msgid ""
"Auto GPU layers estimation is enabled since v1.6.1 when ``n-gpu-layers`` "
"is not specified (default is -1)."
msgstr "v1.6.1부터 n-gpu-layers가 지정되지 않은 경우(기본값: -1), GPU 레이어 수 추정 기능이 자동으로 활성화됩니다."

#: ../../source/user_guide/backends.rst:53
msgid ""
"This feature automatically detects the number of GPU layers (NGL) for the"
" llama.cpp backend. Please be aware that this is not an accurate "
"calculation. Therefore, the ``-ngl`` result might not be the most "
"optimized, and there is still a chance of encountering an out-of-memory "
"error."
msgstr ""
"이 기능은 llama.cpp 백엔드에서 GPU 레이어 수(NGL)를 자동으로 설정합니다. 정확한 계산이 아니므로 ``-ngl`` "
"결과가 최적이 아닐 수 있으며, 여전히 VRAM 부족 오류가 발생할 수 있습니다."

#: ../../source/user_guide/backends.rst:57
msgid ""
"Currently, there is no official implementation for auto ngl. Please refer"
" to the following issues for more information:"
msgstr "현재 자동 NGL은 공식적으로 지원되지 않습니다. 자세한 내용은 아래 이슈를 참조하세요:"

#: ../../source/user_guide/backends.rst:59
msgid "https://github.com/ggml-org/llama.cpp/issues/13860"
msgstr "https://github.com/ggml-org/llama.cpp/issues/13860"

#: ../../source/user_guide/backends.rst:60
msgid "https://github.com/ggml-org/llama.cpp/pull/6502"
msgstr "https://github.com/ggml-org/llama.cpp/pull/6502"

#: ../../source/user_guide/backends.rst:62
msgid ""
"Our implementation is based on the Ollama auto ngl, but there are some "
"differences:"
msgstr "우리의 구현은 Ollama의 자동 NGL을 기반으로 하지만, 몇 가지 차이점이 있습니다:"

#: ../../source/user_guide/backends.rst:64
msgid ""
"We utilize device information detected by `xllamacpp "
"<https://github.com/xorbitsai/xllamacpp>`_."
msgstr ""
"우리는 `xllamacpp <https://github.com/xorbitsai/xllamacpp>`_ 에서 제공하는 장치 정보를 "
"사용합니다."

#: ../../source/user_guide/backends.rst:65
msgid ""
"We have removed support for less popular architectures, these "
"architectures will use the default calculation."
msgstr "일부 흔하지 않은 아키텍처 지원을 제거했으며, 해당 아키텍처에서는 기본 계산 로직이 사용됩니다."

#: ../../source/user_guide/backends.rst:66
msgid ""
"We fall back to offloading all the layers to the GPU if the auto ngl "
"fails."
msgstr "자동 NGL이 실패하면, 전체를 GPU에 로드하려고 시도합니다."

#: ../../source/user_guide/backends.rst:67
msgid ""
"We do not support multimodal projectors embedded into the model GGUF, as "
"this is a very experimental feature."
msgstr "우리는 멀티모달 프로젝터가 모델의 GGUF에 내장되는 것을 지원하지 않습니다. 이 형식의 모델은 현재 실험 단계에 있습니다."

#: ../../source/user_guide/backends.rst:71
msgid "Common Issues"
msgstr "자주 묻는 질문"

#: ../../source/user_guide/backends.rst:73
#, python-brace-format
msgid ""
"**Server error: {'code': 500, 'message': 'failed to process image', "
"'type': 'server_error'}**"
msgstr ""
"**Server error: {'code': 500, 'message': 'failed to process image', "
"'type': 'server_error'}**"

#: ../../source/user_guide/backends.rst:75
#: ../../source/user_guide/backends.rst:99
msgid "The error logs from server:"
msgstr "서버 로그:"

#: ../../source/user_guide/backends.rst:90
msgid ""
"This could be caused by running out of memory. You can try reducing "
"memory usage by decreasing ``n_ctx``."
msgstr "메모리 부족으로 인해 발생할 수 있습니다. ``n_ctx`` 를 줄여서 해결해 보세요."

#: ../../source/user_guide/backends.rst:92
#, python-brace-format
msgid ""
"**Server error: {'code': 400, 'message': 'the request exceeds the "
"available context size. try increasing the context size or enable context"
" shift', 'type': 'invalid_request_error'}**"
msgstr ""
"**Server error: {'code': 400, 'message': 'the request exceeds the "
"available context size. try increasing the context size or enable context"
" shift', 'type': 'invalid_request_error'}**"

#: ../../source/user_guide/backends.rst:94
msgid ""
"If you are using the multimodal feature, the ``ctx_shift`` is disabled by"
" default. Please increase the context size by either increasing ``n_ctx``"
" or reducing ``n_parallel``."
msgstr ""
"만약 multimodal 기능을 사용 중이라면, ``ctx_shift`` 는 기본적으로 비활성화됩니다. ``n_ctx`` 를 "
"늘리거나 ``n_parallel`` 을 줄여 각 슬롯의 컨텍스트 크기를 증가시키십시오."

#: ../../source/user_guide/backends.rst:97
#, python-brace-format
msgid ""
"**Server error: {'code': 500, 'message': 'Input prompt is too big "
"compared to KV size. Please try increasing KV size.', 'type': "
"'server_error'}**"
msgstr ""
"**Server error: {'code': 500, 'message': 'Input prompt is too big "
"compared to KV size. Please try increasing KV size.', 'type': "
"'server_error'}**"

#: ../../source/user_guide/backends.rst:109
msgid ""
"This could be caused by the KV cache allocation failure. You can try to "
"reduce the context size by either reducing ``n_ctx`` or increasing "
"``n_parallel``, or loading a partial model onto the GPU by adjusting "
"``n_gpu_layers``. Be aware that if you are handling inference requests "
"serially, increasing ``n_parallel`` can't improve the latency or "
"throughput."
msgstr ""
"KV cache 생성 실패로 인해 발생할 수 있습니다. ``n_ctx`` 를 줄이거나 ``n_parallel`` 을 늘리거나 "
"``n_gpu_layers`` 매개변수를 조정하여 모델 일부를 GPU에 로드하면 문제를 해결할 수 있습니다. 참고로, 직렬 추론 "
"요청만 처리하는 경우 ``n_parallel`` 을 늘려도 성능이 향상되지 않습니다."

#: ../../source/user_guide/backends.rst:114
msgid "transformers"
msgstr "transformers"

#: ../../source/user_guide/backends.rst:115
msgid ""
"Transformers supports the inference of most state-of-art models. It is "
"the default backend for models in PyTorch format."
msgstr "Transformers는 대부분의 최신 출시 모델을 지원합니다. Pytorch 형식 모델의 기본 엔진입니다."

#: ../../source/user_guide/backends.rst:120
msgid "vLLM"
msgstr "vLLM"

#: ../../source/user_guide/backends.rst:121
msgid "vLLM is a fast and easy-to-use library for LLM inference and serving."
msgstr "vLLM은 매우 효율적이고 사용하기 쉬운 대규모 언어 모델 추론 엔진입니다."

#: ../../source/user_guide/backends.rst:123
msgid "vLLM is fast with:"
msgstr "vLLM은 다음과 같은 특징을 가지고 있습니다:"

#: ../../source/user_guide/backends.rst:125
msgid "State-of-the-art serving throughput"
msgstr "선도적인 추론 처리량"

#: ../../source/user_guide/backends.rst:126
msgid "Efficient management of attention key and value memory with PagedAttention"
msgstr "PagedAttention을 사용하여 어텐션 키와 값 메모리를 효율적으로 관리하기"

#: ../../source/user_guide/backends.rst:127
msgid "Continuous batching of incoming requests"
msgstr "들어오는 요청을 연속 배치 처리합니다."

#: ../../source/user_guide/backends.rst:128
msgid "Optimized CUDA kernels"
msgstr "최적화된 CUDA 커널"

#: ../../source/user_guide/backends.rst:130
msgid ""
"When the following conditions are met, Xinference will choose vLLM as the"
" inference engine:"
msgstr "Xinference가 다음 조건을 충족할 때 자동으로 vLLM을 추론 엔진으로 선택합니다:"

#: ../../source/user_guide/backends.rst:132
msgid ""
"The model format is ``pytorch``, ``gptq``, ``awq``, ``fp4``, ``fp8`` or "
"``bnb``."
msgstr ""
"모델 형식은 ``pytorch`` , ``gptq`` , ``awq`` , ``fp4`` , ``fp8`` 또는 ``bnb`` "
"입니다."

#: ../../source/user_guide/backends.rst:133
msgid "When the model format is ``pytorch``, the quantization is ``none``."
msgstr "모델 형식이 ``pytorch`` 일 경우, 양자화 옵션은 ``none`` 이어야 합니다."

#: ../../source/user_guide/backends.rst:134
msgid "When the model format is ``awq``, the quantization is ``Int4``."
msgstr "모델 형식이 ``awq`` 일 때, 양자화 옵션은 ``Int4`` 여야 합니다."

#: ../../source/user_guide/backends.rst:135
msgid ""
"When the model format is ``gptq``, the quantization is ``Int3``, ``Int4``"
" or ``Int8``."
msgstr "모델 형식이 ``gptq`` 일 때, 양자화 옵션은 ``Int3``, ``Int4`` 또는 ``Int8`` 이어야 합니다."

#: ../../source/user_guide/backends.rst:136
msgid "The system is Linux and has at least one CUDA device"
msgstr "운영 체제는 Linux이며 최소 하나의 CUDA를 지원하는 장치가 있어야 합니다."

#: ../../source/user_guide/backends.rst:137
msgid ""
"The model family (for custom models) / model name (for builtin models) is"
" within the list of models supported by vLLM"
msgstr ""
"사용자 정의 모델의 ``model_family`` 필드와 내장 모델의 ``model_name`` 필드는 vLLM 지원 목록에 "
"있습니다."

#: ../../source/user_guide/backends.rst:139
msgid "Currently, supported model includes:"
msgstr "현재 지원되는 모델은 다음과 같습니다:"

#: ../../source/user_guide/backends.rst:143
msgid ""
"``code-llama``, ``code-llama-instruct``, ``code-llama-python``, "
"``deepseek``, ``deepseek-chat``, ``deepseek-coder``, ``deepseek-coder-"
"instruct``, ``deepseek-r1-distill-llama``, ``gorilla-openfunctions-v2``, "
"``HuatuoGPT-o1-LLaMA-3.1``, ``llama-2``, ``llama-2-chat``, ``llama-3``, "
"``llama-3-instruct``, ``llama-3.1``, ``llama-3.1-instruct``, "
"``llama-3.3-instruct``, ``minicpm5-1b``, ``tiny-llama``, ``wizardcoder-"
"python-v1.0``, ``wizardmath-v1.0``, ``Yi``, ``Yi-1.5``, ``Yi-1.5-chat``, "
"``Yi-1.5-chat-16k``, ``Yi-200k``, ``Yi-chat``"
msgstr ""
"``code-llama``, ``code-llama-instruct``, ``code-llama-python``, "
"``deepseek``, ``deepseek-chat``, ``deepseek-coder``, ``deepseek-coder-"
"instruct``, ``deepseek-r1-distill-llama``, ``gorilla-openfunctions-v2``, "
"``HuatuoGPT-o1-LLaMA-3.1``, ``llama-2``, ``llama-2-chat``, ``llama-3``, "
"``llama-3-instruct``, ``llama-3.1``, ``llama-3.1-instruct``, "
"``llama-3.3-instruct``, ``minicpm5-1b``, ``tiny-llama``, ``wizardcoder-"
"python-v1.0``, ``wizardmath-v1.0``, ``Yi``, ``Yi-1.5``, ``Yi-1.5-chat``, "
"``Yi-1.5-chat-16k``, ``Yi-200k``, ``Yi-chat``"

#: ../../source/user_guide/backends.rst:144
msgid ""
"``codestral-v0.1``, ``mistral-instruct-v0.1``, ``mistral-instruct-v0.2``,"
" ``mistral-instruct-v0.3``, ``mistral-large-instruct``, ``mistral-nemo-"
"instruct``, ``mistral-v0.1``, ``openhermes-2.5``, ``seallm_v2``"
msgstr ""
"``codestral-v0.1``, ``mistral-instruct-v0.1``, ``mistral-instruct-v0.2``,"
" ``mistral-instruct-v0.3``, ``mistral-large-instruct``, ``mistral-nemo-"
"instruct``, ``mistral-v0.1``, ``openhermes-2.5``, ``seallm_v2``"

#: ../../source/user_guide/backends.rst:145
msgid ""
"``Baichuan-M2``, ``codeqwen1.5``, ``codeqwen1.5-chat``, ``deepseek-r1"
"-distill-qwen``, ``DianJin-R1``, ``fin-r1``, ``HuatuoGPT-o1-Qwen2.5``, "
"``KAT-V1``, ``marco-o1``, ``qwen1.5-chat``, ``qwen2-instruct``, "
"``qwen2.5``, ``qwen2.5-coder``, ``qwen2.5-coder-instruct``, "
"``qwen2.5-instruct``, ``qwen2.5-instruct-1m``, ``qwenLong-l1``, ``QwQ-"
"32B``, ``QwQ-32B-Preview``, ``seallms-v3``, ``skywork-or1``, ``skywork-"
"or1-preview``, ``vibethinker``, ``XiYanSQL-QwenCoder-2504``"
msgstr ""
"``Baichuan-M2``, ``codeqwen1.5``, ``codeqwen1.5-chat``, ``deepseek-r1"
"-distill-qwen``, ``DianJin-R1``, ``fin-r1``, ``HuatuoGPT-o1-Qwen2.5``, "
"``KAT-V1``, ``marco-o1``, ``qwen1.5-chat``, ``qwen2-instruct``, "
"``qwen2.5``, ``qwen2.5-coder``, ``qwen2.5-coder-instruct``, "
"``qwen2.5-instruct``, ``qwen2.5-instruct-1m``, ``qwenLong-l1``, ``QwQ-"
"32B``, ``QwQ-32B-Preview``, ``seallms-v3``, ``skywork-or1``, ``skywork-"
"or1-preview``, ``vibethinker``, ``XiYanSQL-QwenCoder-2504``"

#: ../../source/user_guide/backends.rst:146
msgid "``llama-3.2-vision``, ``llama-3.2-vision-instruct``"
msgstr "``llama-3.2-vision``, ``llama-3.2-vision-instruct``"

#: ../../source/user_guide/backends.rst:147
msgid "``baichuan-2``, ``baichuan-2-chat``"
msgstr "``baichuan-2``, ``baichuan-2-chat``"

#: ../../source/user_guide/backends.rst:148
msgid "``InternLM2ForCausalLM``"
msgstr "``InternLM2ForCausalLM``"

#: ../../source/user_guide/backends.rst:149
msgid "``qwen-chat``"
msgstr "``qwen-chat``"

#: ../../source/user_guide/backends.rst:150
msgid ""
"``mixtral-8x22B-instruct-v0.1``, ``mixtral-instruct-v0.1``, "
"``mixtral-v0.1``"
msgstr ""
"``mixtral-8x22B-instruct-v0.1``, ``mixtral-instruct-v0.1``, "
"``mixtral-v0.1``"

#: ../../source/user_guide/backends.rst:151
msgid "``cogagent``"
msgstr "``cogagent``"

#: ../../source/user_guide/backends.rst:152
msgid "``glm-edge-chat``, ``glm4-chat``, ``glm4-chat-1m``"
msgstr "``glm-edge-chat``, ``glm4-chat``, ``glm4-chat-1m``"

#: ../../source/user_guide/backends.rst:153
msgid "``codegeex4``, ``glm-4v``"
msgstr "``codegeex4``, ``glm-4v``"

#: ../../source/user_guide/backends.rst:154
msgid "``seallm_v2.5``"
msgstr "``seallm_v2.5``"

#: ../../source/user_guide/backends.rst:155
msgid "``orion-chat``"
msgstr "``orion-chat``"

#: ../../source/user_guide/backends.rst:156
msgid "``qwen1.5-moe-chat``, ``qwen2-moe-instruct``"
msgstr "``qwen1.5-moe-chat``, ``qwen2-moe-instruct``"

#: ../../source/user_guide/backends.rst:157
msgid "``CohereForCausalLM``"
msgstr "``CohereForCausalLM``"

#: ../../source/user_guide/backends.rst:158
msgid ""
"``deepseek-v2-chat``, ``deepseek-v2-chat-0628``, ``deepseek-v2.5``, "
"``deepseek-vl2``"
msgstr ""
"``deepseek-v2-chat``, ``deepseek-v2-chat-0628``, ``deepseek-v2.5``, "
"``deepseek-vl2``"

#: ../../source/user_guide/backends.rst:159
msgid ""
"``deepseek-prover-v2``, ``deepseek-r1``, ``deepseek-r1-0528``, "
"``deepseek-v3``, ``deepseek-v3-0324``, ``Deepseek-V3.1``, ``moonlight-"
"16b-a3b-instruct``"
msgstr ""
"``deepseek-prover-v2``, ``deepseek-r1``, ``deepseek-r1-0528``, "
"``deepseek-v3``, ``deepseek-v3-0324``, ``Deepseek-V3.1``, ``moonlight-"
"16b-a3b-instruct``"

#: ../../source/user_guide/backends.rst:160
msgid "``deepseek-r1-0528-qwen3``, ``qwen3``"
msgstr "``deepseek-r1-0528-qwen3``, ``qwen3``"

#: ../../source/user_guide/backends.rst:161
msgid "``minicpm3-4b``"
msgstr "``minicpm3-4b``"

#: ../../source/user_guide/backends.rst:162
msgid "``internlm3-instruct``"
msgstr "``internlm3-instruct``"

#: ../../source/user_guide/backends.rst:163
msgid "``gemma-3-1b-it``"
msgstr "``gemma-3-1b-it``"

#: ../../source/user_guide/backends.rst:164
msgid "``glm4-0414``"
msgstr "``glm4-0414``"

#: ../../source/user_guide/backends.rst:165
msgid ""
"``minicpm-2b-dpo-bf16``, ``minicpm-2b-dpo-fp16``, ``minicpm-2b-dpo-"
"fp32``, ``minicpm-2b-sft-bf16``, ``minicpm-2b-sft-fp32``, ``minicpm4``"
msgstr ""
"``minicpm-2b-dpo-bf16``, ``minicpm-2b-dpo-fp16``, ``minicpm-2b-dpo-"
"fp32``, ``minicpm-2b-sft-bf16``, ``minicpm-2b-sft-fp32``, ``minicpm4``"

#: ../../source/user_guide/backends.rst:166
msgid "``Ernie4.5``"
msgstr "``Ernie4.5``"

#: ../../source/user_guide/backends.rst:167
msgid "``Qwen3-Coder``, ``Qwen3-Instruct``, ``Qwen3-Thinking``"
msgstr "``Qwen3-Coder``, ``Qwen3-Instruct``, ``Qwen3-Thinking``"

#: ../../source/user_guide/backends.rst:168
msgid "``glm-4.5``, ``GLM-4.6``, ``GLM-4.7``"
msgstr "``glm-4.5``, ``GLM-4.6``, ``GLM-4.7``"

#: ../../source/user_guide/backends.rst:169
msgid "``gpt-oss``"
msgstr "``gpt-oss``"

#: ../../source/user_guide/backends.rst:170
msgid "``seed-oss``"
msgstr "``seed-oss``"

#: ../../source/user_guide/backends.rst:171
msgid "``Qwen3-Next-Instruct``, ``Qwen3-Next-Thinking``"
msgstr "``Qwen3-Next-Instruct``, ``Qwen3-Next-Thinking``"

#: ../../source/user_guide/backends.rst:172
msgid "``DeepSeek-V3.2``, ``DeepSeek-V3.2-Exp``"
msgstr "``DeepSeek-V3.2``, ``DeepSeek-V3.2-Exp``"

#: ../../source/user_guide/backends.rst:173
msgid "``MiniMax-M2``, ``MiniMax-M2.5``, ``MiniMax-M2.7``"
msgstr "``MiniMax-M2``, ``MiniMax-M2.5``, ``MiniMax-M2.7``"

#: ../../source/user_guide/backends.rst:174
msgid "``GLM-4.7-Flash``"
msgstr "``GLM-4.7-Flash``"

#: ../../source/user_guide/backends.rst:175
msgid "``glm-5``, ``glm-5.1``"
msgstr "``glm-5``, ``glm-5.1``"

#: ../../source/user_guide/backends.rst:176
msgid "``DeepSeek-V4-Flash``, ``DeepSeek-V4-Pro``"
msgstr "``DeepSeek-V4-Flash``, ``DeepSeek-V4-Pro``"

#: ../../source/user_guide/backends.rst:179
msgid ""
"Besides LLMs, vLLM can also serve embedding models. Model families whose "
"names start with ``bge``, ``gte``, ``text2vec``, ``m3e``, ``Qwen3``, or "
"``bce`` can be launched with ``--model-engine vllm`` — see "
":ref:`Embeddings <embed>`."
msgstr ""
"vLLM은 LLM 외에 임베딩 모델도 서빙할 수 있습니다. 이름이 ``bge``, ``gte``, ``text2vec``, "
"``m3e``, ``Qwen3``, ``bce`` 로 시작하는 모델 계열은 ``--model-engine vllm`` 옵션으로 "
"시작할 수 있습니다 — :ref:`임베딩 <embed>` 문서를 참조하세요."

#: ../../source/user_guide/backends.rst:187
msgid "SGLang"
msgstr "SGLang"

#: ../../source/user_guide/backends.rst:188
msgid ""
"`SGLang <https://github.com/sgl-project/sglang>`_ has a high-performance "
"inference runtime with RadixAttention. It significantly accelerates the "
"execution of complex LLM programs by automatic KV cache reuse across "
"multiple calls. And it also supports other common techniques like "
"continuous batching and tensor parallelism."
msgstr ""
"`SGLang <https://github.com/sgl-project/sglang>`_ 는 RadixAttention 기반의 "
"고성능 추론 런타임을 갖추고 있습니다. 여러 호출 간에 KV 캐시를 자동으로 재사용하여 복잡한 LLM 프로그램의 실행을 크게 "
"가속화합니다. 또한 연속 배치 처리 및 텐서 병렬 처리와 같은 다른 일반적인 추론 기술도 지원합니다."

#: ../../source/user_guide/backends.rst:195
msgid "MLX"
msgstr "MLX"

#: ../../source/user_guide/backends.rst:196
msgid ""
"`MLX <https://github.com/ml-explore/mlx-examples/tree/main/llms>`_ "
"provides efficient runtime to run LLM on Apple silicon. It's recommended "
"to use for Mac users when running on Apple silicon if the model has MLX "
"format support."
msgstr ""
"`MLX <https://github.com/ml-explore/mlx-examples/tree/main/llms>`_ 는 "
"Apple Silicon 칩에서 LLM을 효율적으로 실행하는 방법을 제공합니다. 모델이 MLX 형식을 포함하는 경우, Apple "
"Silicon 칩을 사용하는 Mac 사용자에게 MLX 엔진 사용을 권장합니다."

#~ msgid ""
#~ "``code-llama``, ``code-llama-instruct``, "
#~ "``code-llama-python``, ``deepseek``, "
#~ "``deepseek-chat``, ``deepseek-coder``, "
#~ "``deepseek-coder-instruct``, ``deepseek-r1-distill-"
#~ "llama``, ``gorilla-openfunctions-v2``, "
#~ "``HuatuoGPT-o1-LLaMA-3.1``, ``llama-2``, ``llama-2-chat``,"
#~ " ``llama-3``, ``llama-3-instruct``, ``llama-3.1``, "
#~ "``llama-3.1-instruct``, ``llama-3.3-instruct``, ``tiny-"
#~ "llama``, ``wizardcoder-python-v1.0``, "
#~ "``wizardmath-v1.0``, ``Yi``, ``Yi-1.5``, "
#~ "``Yi-1.5-chat``, ``Yi-1.5-chat-16k``, ``Yi-"
#~ "200k``, ``Yi-chat``"
#~ msgstr ""

#~ msgid ""
#~ "``Baichuan-M2``, ``codeqwen1.5``, ``codeqwen1.5-chat``,"
#~ " ``deepseek-r1-distill-qwen``, ``DianJin-R1``, "
#~ "``fin-r1``, ``HuatuoGPT-o1-Qwen2.5``, ``KAT-V1``, "
#~ "``marco-o1``, ``qwen1.5-chat``, ``qwen2-instruct``, "
#~ "``qwen2.5``, ``qwen2.5-coder``, ``qwen2.5-coder-"
#~ "instruct``, ``qwen2.5-instruct``, ``qwen2.5-instruct-"
#~ "1m``, ``qwenLong-l1``, ``QwQ-32B``, ``QwQ-"
#~ "32B-Preview``, ``seallms-v3``, ``skywork-"
#~ "or1``, ``skywork-or1-preview``, ``XiYanSQL-"
#~ "QwenCoder-2504``"
#~ msgstr ""

#~ msgid "``glm-4.5``"
#~ msgstr ""

#~ msgid "``MiniMax-M2``"
#~ msgstr ""
