RADIO-g
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View details ↗DISCOVER & COMPARE
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RADIO-H
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View details ↗RADIO-L
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View details ↗RADIOv2-bigG
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View details ↗RE-USE
NVIDIA** For the commercially friendly model, please refer to NVIDIA-NemotronLabs-RE-USE and NVIDIA-NemotronLabs-RE-USE-laughter ** In universal speech enhancement, the goal is to restore the **quality** of diverse degraded speech while preserving **fidelity**, ensuring that all other factors remain unchanged, e.g., linguistic content, speaker identity, emotion, accent, and other paralinguistic attributes. Inspired by the **distortion–perception trade-off theory**, our proposed single model achieves a good balance between these two objectives and has the following desirable properties: 1. (For **Mamba** setup)Pre-built Docker environments can be downloaded here to simplify **Mamba** setup.
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View details ↗Real-time_RE-USE
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View details ↗retro-48b-instruct-4k
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View details ↗retro-8b-instruct-4k
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View details ↗RFD4-Proteina
NVIDIASequence and structure, designed together. Baker Lab × NVIDIA GenAIR Project page · Code · Documentation · Training data · Studio (ZeroGPU) · Studio (dedicated GPU) RFD4-Proteína is a flow-matching model for biomolecular design. It generates protein sequences and all-atom structures together, conditioned on the molecular context you want to preserve: a binding target, an enzyme motif, a ligand, or selected structural features. This repository is the **canonical native bundle**. `config.json` and `model.safetensors` at the root are the *native* weights, loaded by `Proteina.from_pretrained`.
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View details ↗RFD4-Proteina-Diffusers
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View details ↗RFD4-Proteina-prep
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗RNAPro-Private-Best-500M
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View details ↗RNAPro-Public-Best-500M
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View details ↗RT-DETR-Hand-Detector-v1
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View details ↗SDLLM-AR-1.7B-Base
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View details ↗SDLLM-Duo-1.7B-Base
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View details ↗SDLLM-EsoLM-1.7B-Base
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View details ↗SDLLM-MDLM-1.7B-Base
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View details ↗se_den_sb_16k_small
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View details ↗se_der_sb_16k_small
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View details ↗SegFormer model fine-tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team. SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.
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