Jailbreak-Detector-Large
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 2428
bce-reranker-base_v1
maidalun1020- 中英日韩四个语种，以及中英日韩四个语种的跨语种能力(Multilingual and Crosslingual capability in English, Chinese, Japanese and Korean)； - RAG优化，适配更多真实业务场景(RAG adaptation for more domains, including Education, Law, Finance, Medical, Literature, FAQ, Textbook, Wikipedia, etc.)； - BCEmbedding 适配长文本做rerank(Handle long passages reranking more than 512 limit in BCEmbedding )； - RerankerModel可以提供 **“绝对”分数**，低质量passage过滤阈值推荐0.35或0.4。（RerankerModel provides **"meaningful" (for filtering bad passages with a threshold of 0.35 or 0.4) similarity score**） - **最佳实践（Best practice）** ：embedding召回top50-100片段，reranker对这50-100片段精排，最后取top5-10片段。（1. Get top 50-100 passages with bce-embedding-base_v1 for "`recall`"； 2. Rerank passages with bce-reranker-base_v1 and get top 5-10 for "`precision`" finally. ） - `BCEmbedding`技术博客（ **Technical Blog** ）: 为RAG而生-BCEmbedding技术报告 - Related link for **EmbeddingModel** : bce-embedding-base_v1 - RAG applications: QAnything, ragflow, HuixiangDou, ChatPDF. - Efficient inference framework: ChatLLM.cpp, Xinference, mindnlp (Huawei GPU, 华为GPU).
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查看详情 ↗malay-sentiment-deberta-xsmall
malaysia-ai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 ↗FactCC
manueldeprada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 ↗decider-0.8b
MapikaThe smallest decider: a language model that does not generate text. It reads a **state** (a string or any JSON value) and a set of **typed questions** and returns a probability distribution for every question from a single forward pass: Choice (2-255 options, optionally described), Score (2-10 described levels), Noul (probability of yes). No decoding, no parsing, no output outside the options you defined. Same code, same wire format (`POST /v1/systemone`, TypeSafe Jev's format), same training recipe as the 2B: one epoch of `scripts/train.sh full` from `Qwen/Qwen3.5-0.8B-Base` over the full mixture (1.47M examples, 455M tokens, 4.5 h on one GH200). All six repositories share one interface (`decider.infer.Decider`, `POST /v1/systemone` in TypeSafe's format) and one readout: the letter logits at an answer slot, softmaxed over the options. Pick by size and input. Code, data registry, training scripts, the changelog and the per-version history: https://github.com/Mapika/decider.
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查看详情 ↗decider-12b
Mapika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 ↗decider-2b
MapikaA language model that does not generate text. It reads a state and one or more typed questions, each with an explicit option list, and returns a probability distribution over the options for every question from one forward pass. It is called from software, not chatted with. From v12 the state may include images. **This repository holds v12 (2026-10-20).** v11 stays available under the Hub tag `v11`, with its own card; v10 under `v10`. - **Weights:** the text model at the repository root (Qwen3.5-2B-Base architecture, bf16, 3.8 GB), loaded as before by `decider.infer.Decider`, `decider.serve` and the GGUF builds. `vision/` holds the image part: the vision tower of Qwen3.5-2B-Base (unchanged, 0.6 GB) and the config of the image-text model. The image path reuses the text model's weights. - **Code:** decider-ai 1.10.0 for images; text works with 1.4.0 and later.
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查看详情 ↗decider-2b-GGUF
MapikaGGUF files of Mapika/decider-2b v12, for llama.cpp. decider-2b does not generate text. It reads a state and one or more questions, each with an explicit option list, and returns a probability for every option from one forward pass. See the decider-2b card for what the model is, how it was trained, and where it is weak. **This repository holds the v12 files (2026-10-20).** The v11 files stay available under the Hub tag `v11`. The GGUF files hold the text model only: image input needs the PyTorch weights of Mapika/decider-2b and decider-ai 1.10.0. `decider_config.json` reads yes/no (Noul) answers at temperature 0.05, as the model's own config does (see its card); `decide_gguf.py` reads every question as Choice, at the Choice temperature. The tokenizer, `decider_config.json` (temperatures) and `decide_gguf.py` (the readout on llama.cpp) are in this repository too.
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查看详情 ↗decider-35b-a3b
Mapika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 ↗decider-4b
MapikaA language model that does not generate text. It reads a state and one or more typed questions, each with an explicit option list, and returns a probability distribution over the options for every question from one forward pass. It is called from software, not chatted with. From v3 the state may include images. **This repository holds v3 (2026-10-20).** v2.1 stays available under the Hub tag `v2.1`, with its own card; v2 and v1 under `v2` and `v1`. - **Weights:** the text model at the repository root (Qwen3.5-4B-Base architecture, bf16, 8.4 GB), loaded as before by `decider.infer.Decider`, `decider.serve` and the GGUF builds. `vision/` holds the image part: the vision tower of Qwen3.5-4B-Base (unchanged, 0.6 GB) and the config of the image-text model. The image path reuses the text model's weights. - **Code:** decider-ai 1.10.0 for images; text works with 1.4.0 and later.
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查看详情 ↗decider-4b-GGUF
MapikaGGUF files of Mapika/decider-4b v3, for llama.cpp. decider-4b does not generate text. It reads a state and one or more questions, each with an explicit option list, and returns a probability for every option from one forward pass. See the decider-4b card for what the model is, how it was trained, and where it is weak. **This repository holds the v3 files (2026-10-20).** The v2.1 files stay available under the Hub tag `v2.1`. The GGUF files hold the text model only: image input needs the PyTorch weights of Mapika/decider-4b and decider-ai 1.10.0. `decider_config.json` reads yes/no (Noul) answers at temperature 0.05, as the model's own config does (see its card); `decide_gguf.py` reads every question as Choice, at the Choice temperature. The tokenizer, `decider_config.json` (temperatures) and `decide_gguf.py` (the readout on llama.cpp) are in this repository too.
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查看详情 ↗decider-chat-gemma4-31b
Mapika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 ↗norbert2_sentiment_test1
marcuskdThis model is trained using a self-concatinated dataset consisting of Norwegian Review Corpus dataset (https://github.com/ltgoslo/norec) and a sentiment dataset from huggingface (https://huggingface.co/datasets/sepidmnorozy/Norwegian_sentiment). Its purpose is merely for testing.
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MarieAngeA13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 ↗MiniLM-evidence-types
marieke93This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the evidence types dataset. It achieved the following results on the evaluation set: - Loss: 1.8672 - Macro f1: 0.3726 - Weighted f1: 0.7030 - Accuracy: 0.7161 - Balanced accuracy: 0.3616 The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository BA-Thesis-Information-Science-Persuasion-Strategies The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP
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marklkelly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 ↗bertimbau-large-ner-selective
marquesafonso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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗toxic-comment-model
martin-haThis model is a fine-tuned version of the DistilBERT model to classify toxic comments. This model is intended to use for classify toxic online classifications. However, one limitation of the model is that it performs poorly for some comments that mention a specific identity subgroup, like Muslim. The following table shows a evaluation score for different identity group. You can learn the specific meaning of this metrics here. But basically, those metrics shows how well a model performs for a specific group. The larger the number, the better. The table above shows that the model performs poorly for the muslim and jewish group. In fact, you pass the sentence "Muslims are people who follow or practice Islam, an Abrahamic monotheistic religion." Into the model, the model will classify it as toxic. Be mindful for this type of potential bias.
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massaindustries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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matous-volf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 ↗korean_sentiment
matthewburke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 ↗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。