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working_model

miamorales

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\ generate prompt as danbooru style tags \ a one line caption for the image \ a structured caption format which detects the position of the subjects in the image \ a very detailed description for the image \ image composition analysis mode \ a mixed caption style of more detailed caption and tags, this is extremely useful for FLUX model when using T5XXL and CLIP_L together. A new node in MiaoshouTagger ComfyUI is added to support this instruction. \ Combine the power of mixed caption with analyze. For version 2.0, you will notice the following 1. \ along with a beta node in ComfyUI for partial image analysis 2. A new instruction for \ 3. A much improve accuracy for \ , \ and \ A detailed use and install instruction is already there. (If you have already installed MiaoshouAI Tagger, you need to update the node in ComfyUI Manager first or use git pull to get the latest update.)

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\ generate prompt as danbooru style tags \ a one line caption for the image \ a structured caption format which detects the position of the subjects in the image \ a very detailed description for the image \ image composition analysis mode \ a mixed caption style of more detailed caption and tags, this is extremely useful for FLUX model when using T5XXL and CLIP_L together. A new node in MiaoshouTagger ComfyUI is added to support this instruction. \ Combine the power of mixed caption with analyze. For version 2.0, you will notice the following 1. \ along with a beta node in ComfyUI for partial image analysis 2. A new instruction for \ 3. A much improve accuracy for \ , \ and \ A detailed use and install instruction is already there. (If you have already installed MiaoshouAI Tagger, you need to update the node in ComfyUI Manager first or use git pull to get the latest update.)

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vare1

miatensor

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Uncensored Qwen3.8-27B, published as GGUF quantizations with the multi-token prediction (MTP) head retained and verified. Refusal behaviour has been **substantially reduced**, not eliminated — see Measured behaviour for the numbers. Capabilities, training data, and architecture are otherwise unchanged. > **MTP tensors verified, not assumed.** Abliteration drops the `mtp.*` tensors: the model > is re-saved through transformers, which does not carry the MTP head, while `config.json` > still advertises it. They are grafted back from the base checkpoint and every file is > inspected after quantization — see Method and Verification.

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文本生成
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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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More details about the infinity inference project please refer to the Github: Infinity. To deploy files with the infinity_emb pip package. Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference. If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Michael Feil (infinity at michaelfeil.eu).

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向量与相似度
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]

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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。

基础资料已收录 · 缺失资料后续补充

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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。

1 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。

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