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AI 모델 84759

• claude-sonnet-5-5-thinking↗

  Anthropic

  Anthropic Claude Sonnet 시리즈 모델은 추론, 분석 및 코딩 작업에 적합합니다. 텍스트와 이미지 입력, 텍스트 출력을 지원합니다.

• DepthCrafter↗

  腾讯

  _**Wenbo Hu 1* † , Xiangjun Gao 2* , Xiaoyu Li 1* † , Sijie Zhao 1 , Xiaodong Cun 1 , Yong Zhang 1 , Long Quan 2 , Ying Shan 3, 1 **_ If you find DepthCrafter useful, please help ⭐ the [Github Repo] , which is important to Open-Source projects. Thanks! 🤗 DepthCrafter can generate temporally consistent long depth sequences with fine-grained details for open-world videos, without requiring additional information such as camera poses or optical flow.

• fluently.FluentlyQwen3-Coder-4B-0909-GGUF↗

  DevQuasar

• mms-tts-enb↗

  facebook

• sapiens-depth-0.6b-bfloat16↗

  facebook

• webssl-dino3b-light2b-224↗

  facebook

• Qwen2.5-VL-7B-Abliterated-Caption-it-GGUF↗

  fairy322

• BioLORD-2023-C↗

  FremyCompany

  This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B).

• Devstral-Small-2507-GGUF↗

  gabriellarson

• kieed↗

  Haruka041