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探索 AI 模型 1050
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查看详情 ↗Mistral-NeMo-Minitron-8B-Instruct is a model for generating responses for various text-generation tasks including roleplaying, retrieval augmented generation, and function calling. It is a fine-tuned version of nvidia/Mistral-NeMo-Minitron-8B-Base, which was pruned and distilled from Mistral-NeMo 12B using our LLM compression technique. The model was trained using a multi-stage SFT and preference-based alignment technique with NeMo Aligner. For details on the alignment technique, please refer to the Nemotron-4 340B Technical Report. The model supports a context length of 8,192 tokens. **Model Dates:** Mistral-NeMo-Minitron-8B-Instruct was trained between August 2024 and September 2024. Mistral-NeMo-Minitron-8B-Instruct uses a model embedding size of 4096, 32 attention heads, MLP intermediate dimension of 11520, with 40 layers in total. Additionally, it uses Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE).
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查看详情 ↗mit-b0
NVIDIASegFormer encoder fine-tuned on Imagenet-1k. 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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查看详情 ↗mit-b1
NVIDIASegFormer encoder fine-tuned on Imagenet-1k. 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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查看详情 ↗mit-b2
NVIDIASegFormer encoder fine-tuned on Imagenet-1k. 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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查看详情 ↗mit-b3
NVIDIASegFormer encoder fine-tuned on Imagenet-1k. 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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查看详情 ↗mit-b4
NVIDIASegFormer encoder fine-tuned on Imagenet-1k. 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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查看详情 ↗mit-b5
NVIDIASegFormer encoder fine-tuned on Imagenet-1k. 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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查看详情 ↗MM-Embed
NVIDIA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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查看详情 ↗MotionBricks
NVIDIA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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查看详情 ↗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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查看详情 ↗Muse-Glimmer-30B-NVFP4
NVIDIAThe NVIDIA Muse-Glimmer-30B NVFP4 model is the quantized version of Meta Superintelligence Lab's Muse-Glimmer-30B model, which is an auto-regressive language model that uses an optimized transformer architecture. Muse Glimmer is a dense causal transformer with a dedicated perception encoder for interleaved text and image input, purpose-built for local agentic tasks including multi-step reasoning, tool use, and multimodal understanding. For more information, please check here. The NVIDIA Muse-Glimmer-30B 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 (Muse-Glimmer-30B) Model Card from Meta. Developers building autonomous agentic systems, multi-step planning agents, coding agents, and multimodal reasoning applications requiring reliable tool use and failure recovery on consumer hardware.
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查看详情 ↗music-flamingo-2601-hf
NVIDIAMusic Flamingo (MF) is a fully open, state-of-the-art Large Audio-Language Model (LALM) designed to advance music (including song) understanding in foundational audio models. MF brings together innovations in: Extensive evaluations confirm Music Flamingo's effectiveness, setting new benchmarks on over 10+ public music understanding and reasoning tasks. Music Flamingo (MF) is supported in 🤗 Transformers. To run the model, first install Transformers from our fork (we are awaiting HF merge):
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查看详情 ↗music-flamingo-hf
NVIDIA🚨 **Note:** We have released a new and more powerful checkpoint here. Music Flamingo (MF) is a fully open, state-of-the-art Large Audio-Language Model (LALM) designed to advance music (including song) understanding in foundational audio models. MF brings together innovations in: - Deep music understanding across songs and instrumentals. - Rich, theory-aware captions and question answering (harmony, structure, timbre, lyrics, cultural context). - Reasoning-centric training using chain-of-thought + reinforcement learning with custom rewards for step-by-step reasoning. - Long-form song reasoning over full-length, multicultural audio (extended context).
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查看详情 ↗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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查看详情 ↗NeKo-v0-post-correction
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查看详情 ↗nemo-megatron-gpt-1.3B
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查看详情 ↗nemo-megatron-gpt-20B
NVIDIA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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查看详情 ↗nemo-megatron-gpt-5B
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查看详情 ↗nemo-megatron-mt5-3B
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。