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84,925 款模型档案13150 家厂商 / 发布者45 个能力分类465 款有可比报价 · 30 款有公开评测

T文本生成 ▧图像生成 ▷视频生成 ⌘代码生成 ◖语音合成 ♫音乐生成 ⬡3D 建模 ▤文档分析

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robeczech-base

ufal

The tokenizer in the initial release (a) contained a hole (51959 did not correspond to any token), and (b) mapped several tokens (unseen during training but required by the BBPE tokenizer) to the same ID as the `[UNK]` token (3). That sometimes caused problems, as in https://huggingface.co/ufal/robeczech-base/discussions/4. See https://huggingface.co/ufal/robeczech-base/discussions/4#64b8f6a7f1f8e6ea5860b314 for more information. In version 1.1, the tokenizer was modified by (a) removing the hole, (b) mapping all tokens to a unique ID. That also required increasing the vocabulary size and embeddings weights (by replicating the embedding of the `[UNK]` token). Without finetuning, version 1.1 and version 1.0 gives exactly the same embeddings on any input (apart from the pooler missing in v1.1), and the tokens in version 1.0 that mapped to a different ID than the `[UNK]` token map to the same ID in version 1.1. However, the sizes of the embeddings (and LM head weights and biases) are different, so the weights of the version 1.1 are not compatible with the configuration of version 1.0 and vice versa.

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填空与掩码

输入文本→ 输出文本

价格待核验多模态与渠道费用须分别核对

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vit-historical-page

ufal

Fine-tuned image classifiers that sort scanned archive pages into eleven content categories, so that each page can be sent to the processing it needs: OCR for printed or typed text, handwriting recognition for manuscripts, table extraction for forms, image extraction for drawings and photographs. They look at the page image only, so the language of the page does not matter. The code, the command line and the HTTP service are in atrium-page-classification [^3]. Every version is a **revision** (branch) of this repository [^1]. They share the eleven categories and differ in their training data and base model: ViT [^2] [^6] [^7], EfficientNetV2 [^8] [^9] [^11] or RegNetY [^12]. The current generation is **`vX.4`**: five base models, retrained on the licence-compatible public dataset [^10]. **Name the revision when you load a model:** `main` still holds `v4.3`, the generation before. Each model was scored on the test partition of its own fold of the dataset (folds 1, 1, 2, 2 and 5; 4,791–4,795 pages each), on 22 October 2026. The five together, averaged, are the code's ensemble (`--best`, and the default of its HTTP service).

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图像分类

输入图像→ 输出待核验

价格待核验多模态与渠道费用须分别核对

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

每月输入 Token 10 M每月输出 Token 2 M1 M = 100 万 Token · 美元计价
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

选型与常见问题

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。