NFT-32B
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NFT-7B
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View details ↗NitroGen
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View details ↗NL-Diffusion-Image
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View details ↗NLD-Uni
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View details ↗NV-CodonFM-Encodon-1B-v1
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View details ↗CodonFM predicts masked codons in mRNA sequences from codon-level context to enable variant effect interpretation and codon optimization as part of NVIDIA’s CodonFM Encodon family. For this family of models we have 4 models. The first set of 3 models are with randomly masked tokens with 80 million, 600 million and 1 Billion parameter. The fourth model is with 1 Billion parameters but is trained with codon frequency aware masking. Github 10/27/2025 via https://github.com/NVIDIA-Digital-Bio/CodonFM Hugging Face 10/27/2025 via: - Random Mask - https://huggingface.co/nvidia/NV-CodonFM-Encodon-1B-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-600M-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-80M-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-1B-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-600M-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-80M-v1 - Codon Frequency Aware Mask - https://huggingface.co/nvidia/NV-CodonFM-Encodon-Cdwt-1B-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-Cdwt-1B-v1 The NVIDIA CodonFM Encodon family features **Transformer-based architectures** tailored for codon-level sequence modeling in mRNA. Each model applies a **masked language modeling (MLM)** objective to predict masked codons from surrounding context of 2046 codons, enabling genome-scale codon optimization and synonymous variant interpretation.
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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 ↗NV-DualBind-1M-v1
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View details ↗NV-Embed-v1
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View details ↗NV-Embed-v2
NVIDIAWe present NV-Embed-v2, a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark (MTEB benchmark)(as of Aug 30, 2024) with a score of 72.31 across 56 text embedding tasks. It also holds the No. 1 in the retrieval sub-category (a score of 62.65 across 15 tasks) in the leaderboard, which is essential to the development of RAG technology. NV-Embed-v2 presents several new designs, including having the LLM attend to latent vectors for better pooled embedding output, and demonstrating a two-staged instruction tuning method to enhance the accuracy of both retrieval and non-retrieval tasks. Additionally, NV-Embed-v2 incorporates a novel hard-negative mining methods that take into account the positive relevance score for better false negatives removal. For more technical details, refer to our paper: NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.
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View details ↗NV-EmbedCode-7b-v1
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View details ↗NV-Generate-CT
NVIDIANV-Generate-CT is a state-of-the-art three-dimensional (3D) Latent Diffusion Model designed for generating high-quality synthetic CT images with or without anatomical annotations. This AI model excels in data augmentation and creating realistic medical imaging data to supplement limited datasets due to privacy concerns or rare conditions. It can also significantly enhance the performance of other medical imaging AI models by generating diverse and realistic training data. Generates high-resolution 3D CT images up to 512 × 512 × 768 voxels Supports variable voxel sizes ranging from 0.5mm to 5.0mm Capable of annotating up to 127 anatomical classes, including organs and tumors Allows controllable anatomy size for 10 specific classes Produces paired segmentation masks By providing these capabilities, MAISI is a valuable tool for researchers advancing AI applications in healthcare. However, it is important to note that this model is intended for research purposes only and not for clinical usage.
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View details ↗NV-Generate-MR
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View details ↗NV-Generate-MR-Brain
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View details ↗NV-GenMol-89M-v1
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View details ↗NV-GenMol-89M-v2
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View details ↗NV-JEPA-DNA-DNABERT2
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View details ↗NV-JEPA-DNA-HyenaDNA
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