# 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.
#
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: de\n"
"Language-Team: de <LL@li.org>\n"
"Plural-Forms: nplurals=2; plural=(n != 1);\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 "Inferenz-Engine"

#: ../../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 ""
"Xinference unterstützt für verschiedene Modelle unterschiedliche "
"Inferenz-Engines. Nachdem der Benutzer ein Modell ausgewählt hat, wählt "
"Xinference automatisch die passende Engine aus."

#: ../../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 ""
"Xinference unterstützt derzeit das von Xinference-Team entwickelte "
"`xllamacpp <https://github.com/xorbitsai/xllamacpp>`_ als llama.cpp-"
"Backend. `llama.cpp` basiert auf der Tensor-Bibliothek `ggml` und "
"unterstützt die Inferenz von LLaMA-Serienmodellen und deren Varianten."

#: ../../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 ""
"Seit Xinference v1.5.0 ist ``xllamacpp`` die Standardoption für "
"llama.cpp, ``llama-cpp-python`` ist veraltet; ab Xinference v1.6.0 wurde "
"``llama-cpp-python`` entfernt."

#: ../../source/user_guide/backends.rst:25
msgid "Automatic GPU wheel selection"
msgstr "Automatische Auswahl des GPU-Wheels"

#: ../../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 "Wenn virtuelle Umgebungen pro Modell aktiviert sind und eine nutzbare NVIDIA-GPU vorhanden ist, installiert Xinference automatisch das zur erkannten CUDA-Reihe passende ``xllamacpp``-Wheel. CUDA 12.8 und höher innerhalb der 12.x-Reihe verwendet den ``cu128``-Wheel-Index, CUDA 13.x den ``cu132``-Wheel-Index. Reine CPU-Systeme und nicht unterstützte CUDA-Versionen verwenden weiterhin das standardmäßige CPU-Wheel von PyPI. Für diesen Startpfad ist kein separater Installationsbefehl für ``xllamacpp`` GPU erforderlich."

#: ../../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 ""
"Informationen zu allen konfigurierbaren llama.cpp-Parametern finden Sie "
"in der Definition der Struktur ``common_params`` in ``llama.cpp`` "
"`common.h <https://github.com/ggml-"
"org/llama.cpp/blob/master/common/common.h>`_"

#: ../../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 ""
"Es kann Parameter mit mehreren Verschachtelungsebenen geben. Zum Beispiel"
" ``sampling.top_k``. Bitte verwenden Sie ``.``, um verschachtelte "
"Parameter zu trennen."

#: ../../source/user_guide/backends.rst:41
msgid "Here is an example of setting nested sampling parameters in WebUI:"
msgstr ""
"Hier ist ein Beispiel für die Einstellung der verschachtelten Sampling-"
"Parameter in der WebUI:"

#: ../../source/user_guide/backends.rst:48
msgid "Auto NGL"
msgstr "Auto 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 ""
"Seit v1.6.1 ist die automatische Schätzung der GPU-Layer aktiviert, wenn "
"``n-gpu-layers`` nicht angegeben ist (Standard ist -1)."

#: ../../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 ""
"Diese Funktion kann die Anzahl der GPU-Layer (NGL) automatisch für das "
"llama.cpp-Backend festlegen. Beachten Sie, dass dies keine präzise "
"Berechnung ist, daher ist das ``-ngl``-Ergebnis möglicherweise nicht "
"optimal, und es können weiterhin Fehler aufgrund unzureichenden "
"Videospeichers auftreten."

#: ../../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 ""
"Derzeit gibt es keine offizielle Implementierung für automatisches NGL. "
"Weitere Informationen finden Sie in den folgenden Issues:"

#: ../../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 ""
"Unsere Implementierung basiert auf Ollamas automatischem NGL, weist "
"jedoch einige Unterschiede auf:"

#: ../../source/user_guide/backends.rst:64
msgid ""
"We utilize device information detected by `xllamacpp "
"<https://github.com/xorbitsai/xllamacpp>`_."
msgstr ""
"Wir verwenden die Geräteinformationen, die von `xllamacpp "
"<https://github.com/xorbitsai/xllamacpp>`_ bereitgestellt werden."

#: ../../source/user_guide/backends.rst:65
msgid ""
"We have removed support for less popular architectures, these "
"architectures will use the default calculation."
msgstr ""
"Wir haben die Unterstützung für einige ungewöhnliche Architekturen "
"entfernt, bei denen die Standardberechnungslogik verwendet wurde."

#: ../../source/user_guide/backends.rst:66
msgid ""
"We fall back to offloading all the layers to the GPU if the auto ngl "
"fails."
msgstr ""
"Wenn das automatische NGL fehlschlägt, versuchen wir, alles auf die GPU "
"zu laden."

#: ../../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 ""
"Wir unterstützen keine GGUF-Modelle mit eingebetteten multimodalen "
"Projektoren. Dieses Format befindet sich derzeit noch in der "
"Experimentierphase."

#: ../../source/user_guide/backends.rst:71
msgid "Common Issues"
msgstr "Häufig gestellte Fragen"

#: ../../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 "Server-Logs:"

#: ../../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 ""
"Dies kann durch unzureichenden Speicher verursacht werden. Sie können "
"versuchen, den Speicherverbrauch zu reduzieren, indem Sie ``n_ctx`` "
"verringern."

#: ../../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 ""
"Wenn Sie die Multimodal-Funktion verwenden, ist ``ctx_shift`` "
"standardmäßig deaktiviert. Versuchen Sie, ``n_ctx`` zu erhöhen oder "
"``n_parallel`` zu verringern, um die Kontextgröße pro Slot zu vergrößern."

#: ../../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 ""
"Dies kann aufgrund eines fehlgeschlagenen KV-Cache-Aufbaus auftreten. Sie"
" können das Problem lösen, indem Sie ``n_ctx`` verkleinern, "
"``n_parallel`` erhöhen oder den Parameter ``n_gpu_layers`` anpassen, um "
"einen Teil des Modells auf die GPU zu laden. Beachten Sie, dass eine "
"Erhöhung von ``n_parallel`` keine Leistungssteigerung bringt, wenn Sie "
"nur serielle Inferenzanfragen verarbeiten."

#: ../../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 ""
"Transformers unterstützt die überwiegende Mehrheit der neu "
"veröffentlichten Modelle. Es ist die Standard-Engine für Modelle im "
"PyTorch-Format."

#: ../../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 ""
"vLLM ist eine äußerst effiziente und benutzerfreundliche Inferenz-Engine "
"für große Sprachmodelle."

#: ../../source/user_guide/backends.rst:123
msgid "vLLM is fast with:"
msgstr "vLLM hat die folgenden Eigenschaften:"

#: ../../source/user_guide/backends.rst:125
msgid "State-of-the-art serving throughput"
msgstr "Führender Inferenz-Durchsatz"

#: ../../source/user_guide/backends.rst:126
msgid "Efficient management of attention key and value memory with PagedAttention"
msgstr "Verwaltung von Attention-Key- und Value-Speichern mit PagedAttention"

#: ../../source/user_guide/backends.rst:127
msgid "Continuous batching of incoming requests"
msgstr "Kontinuierliches Batch-Processing eingehender Anfragen"

#: ../../source/user_guide/backends.rst:128
msgid "Optimized CUDA kernels"
msgstr "Optimierte CUDA-Kernel"

#: ../../source/user_guide/backends.rst:130
msgid ""
"When the following conditions are met, Xinference will choose vLLM as the"
" inference engine:"
msgstr ""
"Wenn die folgenden Bedingungen erfüllt sind, wählt Xinference automatisch"
" vLLM als Inference-Engine aus:"

#: ../../source/user_guide/backends.rst:132
msgid ""
"The model format is ``pytorch``, ``gptq``, ``awq``, ``fp4``, ``fp8`` or "
"``bnb``."
msgstr ""
"Das Modellformat ist ``pytorch``, ``gptq``, ``awq``, ``fp4``, ``fp8`` "
"oder ``bnb``."

#: ../../source/user_guide/backends.rst:133
msgid "When the model format is ``pytorch``, the quantization is ``none``."
msgstr ""
"Wenn das Modellformat ``pytorch`` ist, muss die Quantisierungsoption "
"``none`` sein."

#: ../../source/user_guide/backends.rst:134
msgid "When the model format is ``awq``, the quantization is ``Int4``."
msgstr ""
"Wenn das Modellformat ``awq`` ist, muss die Quantisierungsoption ``Int4``"
" sein."

#: ../../source/user_guide/backends.rst:135
msgid ""
"When the model format is ``gptq``, the quantization is ``Int3``, ``Int4``"
" or ``Int8``."
msgstr ""
"Wenn das Modellformat ``gptq`` ist, muss die Quantisierungsoption "
"``Int3``, ``Int4`` oder ``Int8`` sein."

#: ../../source/user_guide/backends.rst:136
msgid "The system is Linux and has at least one CUDA device"
msgstr ""
"Das Betriebssystem ist Linux und es gibt mindestens ein CUDA-fähiges "
"Gerät."

#: ../../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 ""
"Das ``model_family``-Feld benutzerdefinierter Modelle und das "
"``model_name``-Feld integrierter Modelle sind in der Unterstützungsliste "
"von vLLM enthalten."

#: ../../source/user_guide/backends.rst:139
msgid "Currently, supported model includes:"
msgstr "Derzeit umfassen die unterstützten Modelle:"

#: ../../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 ""
"Neben LLMs kann vLLM auch Embedding-Modelle bereitstellen. "
"Modellfamilien, deren Namen mit ``bge``, ``gte``, ``text2vec``, ``m3e``, "
"``Qwen3`` oder ``bce`` beginnen, können mit ``--model-engine vllm`` "
"gestartet werden — siehe :ref:`Embeddings <embed>`."

#: ../../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>`_ verfügt über eine "
"leistungsstarke Inferenz-Laufzeitumgebung basierend auf RadixAttention. "
"Sie beschleunigt die Ausführung komplexer LLM-Programme erheblich, indem "
"sie den KV-Cache automatisch über mehrere Aufrufe hinweg wiederverwendet."
" Sie unterstützt zudem andere gängige Inferenztechniken wie "
"kontinuierliches Batching und Tensor-Parallelisierung."

#: ../../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>`_ bietet"
" eine effiziente Möglichkeit, LLMs auf Apple Silicon-Chips auszuführen. "
"Wenn ein Modell im MLX-Format vorliegt, wird Mac-Benutzern mit Apple "
"Silicon-Chips die Verwendung der MLX-Engine empfohlen."

#~ 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 ""
