strands-decider-2B-hobson-v19-GGUF
fabricant451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 ↗DISCOVER & COMPARE
探索 AI 模型 2428
bert-base-uncased-ag_news
fabriceyhc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 ↗bert-base-uncased-amazon_polarity
fabriceyhc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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dinov2-base
facebookVision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features without Supervision by Oquab et al. and first released in this repository. Disclaimer: The team releasing DINOv2 did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion.
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查看详情 ↗dinov2-large
facebookVision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features without Supervision by Oquab et al. and first released in this repository. Disclaimer: The team releasing DINOv2 did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion.
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查看详情 ↗dinov2-small
facebookVision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper DINOv2: Learning Robust Visual Features without Supervision by Oquab et al. and first released in this repository. Disclaimer: The team releasing DINOv2 did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion.
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查看详情 ↗dinov3-vitl16-pretrain-lvd1689m
facebook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 ↗fasttext-language-identification
facebookfastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in this paper. The official website can be found here. This LID (Language IDentification) model is used to predict the language of the input text, and the hosted version (`lid218e`) was released as part of the NLLB project and can detect 217 languages. You can find older versions (ones that can identify 157 languages) on the official fastText website. fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
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查看详情 ↗The R4 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!
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查看详情 ↗tart-full-flan-t5-xl
facebook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 ↗w2v-bert-2.0
facebookWe are open-sourcing our Conformer-based W2v-BERT 2.0 speech encoder as described in Section 3.2.1 of the paper, which is at the core of our Seamless models. This model was pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages. It requires finetuning to be used for downstream tasks such as Automatic Speech Recognition (ASR), or Audio Classification. **This model and its training are supported by 🤗 Transformers, more on it in the docs.**
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查看详情 ↗roberta-large-mnli
FacebookAI- Model Details - How To Get Started With the Model - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors **Model Description:** roberta-large-mnli is the RoBERTa large model fine-tuned on the Multi-Genre Natural Language Inference (MNLI) corpus. The model is a pretrained model on English language text using a masked language modeling (MLM) objective. - **Developed by:** See GitHub Repo for model developers - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** MIT - **Parent Model:** This model is a fine-tuned version of the RoBERTa large model. Users should see the RoBERTa large model card for relevant information. - **Resources for more information:** - Research Paper - GitHub Repo
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查看详情 ↗1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training 5. Evaluation 6. Environmental Impact 7. Technical Specifications 8. Citation 9. Model Card Authors 10. How To Get Started With the Model The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is XLM-RoBERTa-large fine-tuned with the conll2003 dataset in English. - **Developed by:** See associated paper - **Model type:** Multi-lingual language model - **Language(s) (NLP) or Countries (images):** XLM-RoBERTa is a multilingual model trained on 100 different languages; see GitHub Repo for full list; model is fine-tuned on a dataset in English - **License:** More information needed - **Related Models:** RoBERTa, XLM - **Parent Model:** XLM-RoBERTa-large - **Resources for more information:** -GitHub Repo -Associated Paper
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查看详情 ↗1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training 5. Evaluation 6. Environmental Impact 7. Technical Specifications 8. Citation 9. Model Card Authors 10. How To Get Started With the Model The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is XLM-RoBERTa-large fine-tuned with the conll2003 dataset in German. The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text.
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查看详情 ↗roberta-base-ai-text-detection-v1
fakespot-aiSee FakespotAILabs/ApolloDFT for more details and a technical report of the model and experiments we conducted. For better performance, you should apply the `clean_text` function in utils.py. - The model's score represents an estimation of the likelihood of the input text being AI-generated or human-written, rather than indicating the proportion of the text that is AI-generated or human-written. - The accuracy and performance of the model generally improve with longer text inputs.
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查看详情 ↗OTel-Reranker-0.6B
farbodtavakkoli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 ↗gliguard-LLMGuardrails-300M
fastinoGLiGuard is a compact, encoder-based guardrail model for LLM safety moderation built on the `GLiNER2` interface. Instead of generating moderation verdicts autoregressively, it treats safety as structured classification: you provide task names and candidate labels at inference time, and the model scores all requested moderation tasks in a single bidirectional encoder pass. This model is CPU-first and can be used for effective protection against prompt hack injection, jailbreaking, and identifying harmful content. *GLiGuard’s accuracy remains competitive with guardrail models that are 23 to 90 times its size while running up to 16 times faster with 17 times lower latency.* The released checkpoint, `fastino/gliguard-LLMGuardrails-300M`, is a 0.3B-parameter model designed for fast local inference.
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查看详情 ↗GLiNER2.5-Decide
fastino**The 340M English classification model in the GLiNER2.5 family.** Pass any label set at call time: intent, routing, sentiment, priority, policy, and multi-label tags, in a single forward pass. No prompt template. No generated tokens. Load it with `AutoExtractor` and ship it locally. A single call can score several heads at once. Single-label tasks return one string. Multi-label tasks return every label above the threshold. Exact-match accuracy on `fastino/fast-decisions`: 17 domains, 300 held-out examples each, with the same text and candidate labels for every model.
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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。