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AI 모델 84759

• GCViT↗

  NVIDIA

• GR00T-H↗

  NVIDIA

• Mistral-Medium-3.5-128B-NVFP4↗

  NVIDIA

  Mistral Medium 3.5 128B is Mistral AI's first flagship merged model. It is a dense 128B model with a 256k context window, handling instruction-following, reasoning, and coding in a single set of weights. Mistral Medium 3.5 replaces its predecessor Mistral Medium 3.1 and Magistral in Le Chat. It also replaces Devstral 2 in our coding agent Vibe. Concretely, expect better performance for instruct, reasoning and coding tasks in a new unified model in comparison with our previous released models. The NVIDIA Mistral-Medium-3.5-128B NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Mistral-Medium-3.5-128B Model Card **GOVERNING TERMS:** **GOVERNING TERMS:** Use of this model is governed by Mistral’s Modified MIT license. To deploy and customize the model in your environment, please contact Mistral[https://mistral.ai/contact/].

• parakeet-ctc-0.6b-Vietnamese↗

  NVIDIA

• gpt-oss-20b↗

  oki0ki

• MiniCPM4-8B↗

  openbmb

  MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems. - MiniCPM4-8B: The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens. (** **Note**: In vLLM's chat API, `add_special_tokens` is `False` by default. This means important special tokens—such as the beginning-of-sequence (BOS) token—will not be added automatically. To ensure the input prompt is correctly formatted for the model, you should explicitly set `extra_body={"add_special_tokens": True}`. On two typical end-side chips, Jetson AGX Orin and RTX 4090, MiniCPM4 demonstrates significantly faster processing speed compared to similar-size models in long text processing tasks. As text length increases, MiniCPM4's efficiency advantage becomes more pronounced. On the Jetson AGX Orin platform, compared to Qwen3-8B, MiniCPM4 achieves approximately 7x decoding speed improvement. MiniCPM4 launches end-side versions with 8B and 0.5B parameter scales, both achieving best-in-class performance in their respective categories.

• OpenMed-ZeroShot-NER-Species-Small-166M↗

  OpenMed

  **Specialized model for Species Entity Recognition - Species and organism names** Specialized in **species and organism mentions** with robust handling of scientific/common names and abbreviations.Applies to **biodiversity mining**, **metagenomics reporting**, and **taxonomy-aware literature curation**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.

• PP-LCNet_x1_0_doc_ori_safetensors↗

  PaddlePaddle

• FireRed-OCR-GGUF↗

  prithivMLmods

• tencent-WeVisOne-VL-4B-GGUF↗

  prithivMLmods