medgemma-27b-text-it
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84999
medgemma-4b-it
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查看详情 ↗medgemma-4b-pt
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查看详情 ↗medsiglip-448
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查看详情 ↗metricx-23-large-v2p0
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查看详情 ↗metricx-23-qe-large-v2p0
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查看详情 ↗metricx-23-qe-xl-v2p0
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查看详情 ↗metricx-23-qe-xxl-v2p0
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查看详情 ↗metricx-23-xl-v2p0
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查看详情 ↗metricx-23-xxl-v2p0
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查看详情 ↗The repository contains the code for running inference on MetricX-24 models, a family of models for automatic evaluation of translations that were proposed in the WMT'24 Metrics Shared Task submission MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. There are 3 MetricX-24 models available on Hugging Face that vary in the number of parameters. Unlike the MetricX-23 models, the MetricX-24 models are all hybrid models that can do both reference-based and reference-free (also known as quality estimation, or QE) inference: We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case.
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查看详情 ↗The repository contains the code for running inference on MetricX-24 models, a family of models for automatic evaluation of translations that were proposed in the WMT'24 Metrics Shared Task submission MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. There are 3 MetricX-24 models available on Hugging Face that vary in the number of parameters. Unlike the MetricX-23 models, the MetricX-24 models are all hybrid models that can do both reference-based and reference-free (also known as quality estimation, or QE) inference: We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case.
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查看详情 ↗> ℹ️ For the bfloat16 variant of this model, see MetricX-24 (XL, bfloat16). The repository contains the code for running inference on MetricX-24 models, a family of models for automatic evaluation of translations that were proposed in the WMT'24 Metrics Shared Task submission MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. There are 3 MetricX-24 models available on Hugging Face that vary in the number of parameters. Unlike the MetricX-23 models, the MetricX-24 models are all hybrid models that can do both reference-based and reference-free (also known as quality estimation, or QE) inference:
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗The repository contains the code for running inference on MetricX-24 models, a family of models for automatic evaluation of translations that were proposed in the WMT'24 Metrics Shared Task submission MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. There are 3 MetricX-24 models available on Hugging Face that vary in the number of parameters. Unlike the MetricX-23 models, the MetricX-24 models are all hybrid models that can do both reference-based and reference-free (also known as quality estimation, or QE) inference: We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗The repository contains the code for running inference on MetricX-24 models, a family of models for automatic evaluation of translations that were proposed in the WMT'24 Metrics Shared Task submission MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. There are 3 MetricX-24 models available on Hugging Face that vary in the number of parameters. Unlike the MetricX-23 models, the MetricX-24 models are all hybrid models that can do both reference-based and reference-free (also known as quality estimation, or QE) inference: We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗The repository contains the code for running inference on MetricX-24 models, a family of models for automatic evaluation of translations that were proposed in the WMT'24 Metrics Shared Task submission MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task. The models were trained in T5X and then converted for use in PyTorch. There are 3 MetricX-24 models available on Hugging Face that vary in the number of parameters. Unlike the MetricX-23 models, the MetricX-24 models are all hybrid models that can do both reference-based and reference-free (also known as quality estimation, or QE) inference: We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case.
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查看详情 ↗mobilebert-uncased
GoogleMobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. This checkpoint is the original MobileBert Optimized Uncased English: uncased_L-24_H-128_B-512_A-4_F-4_OPT checkpoint.
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查看详情 ↗mobilenet_v1_0.75_192
GoogleMobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Howard et al, and first released in this repository. Disclaimer: The team releasing MobileNet V1 did not write a model card for this model so this model card has been written by the Hugging Face team. > MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
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查看详情 ↗mobilenet_v1_1.0_224
GoogleMobileNet V1 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Howard et al, and first released in this repository. Disclaimer: The team releasing MobileNet V1 did not write a model card for this model so this model card has been written by the Hugging Face team. > MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
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查看详情 ↗mobilenet_v2_0.35_96
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查看详情 ↗mobilenet_v2_0.75_160
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查看详情 ↗mobilenet_v2_1.0_224
GoogleMobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in this repository. Disclaimer: The team releasing MobileNet V2 did not write a model card for this model so this model card has been written by the Hugging Face team. From the original README:
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查看详情 ↗mobilenet_v2_1.4_224
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查看详情 ↗mt5-base
GooglemT5 is pretrained on the mC4 corpus, covering 101 languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu. **Note**: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
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价格待核验多模态与渠道费用须分别核对
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。