dante-v1
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View details ↗DISCOVER & COMPARE
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MiniMax-H3-ComfyUI
EllipsesMark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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View details ↗Minimax-h3_Singularity
EllipsesMark**Minimax-h3_Singularity** is a comprehensive fine-tuned fusion model specialized in enhancing the capabilities of **MiniMax-H3**. Designed as a versatile **multimodal video generation model**, it natively supports **Text-to-Video (T2V)**, **Image-to-Video (I2V)**, **Reference-to-Video (Ref2V)**, and **Video-to-Video (V2V)** workflows within **ComfyUI**. Built upon a strategic fusion of key checkpoints (including `ref`, `fl`, `b25-49`, etc.), this model underwent deep high-step fine-tuning. To preserve the original model's foundational strengths and broad generalization while solving artifacts introduced by high-step training, we spent **3 full days on precise model pruning and weight optimization**. The result is a clean, sharp, and highly dynamic video generation model. This model is fully compatible with **ComfyUI** and supports: * **Text-to-Video (T2V)** * **Image-to-Video (I2V)** * **Reference-to-Video (Ref2V)** * **Video-to-Video (V2V)**
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View details ↗Qwen-Image-2.1
EllipsesMark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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View details ↗ankh-base
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View details ↗ankh-large
ElnaggarLab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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View details ↗ankh3-large
ElnaggarLab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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View details ↗ankh3-xl
ElnaggarLab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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View details ↗humanizer
ElonCoding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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View details ↗Glimpse
elorianGlimpse is a multimodal visual reasoning model from Elorian, built for visual understanding and complex visual reasoning in the physical world. It reasons directly over images and video rather than...
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$0.6 / M input tokensOpenRouter · elorian/glimpse
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View details ↗bleurt-base-128
Elron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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View details ↗bleurt-tiny-512
Elron- **Developed by:** Elron Bandel, Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research - **Shared by [Optional]:** Elron Bandel - **Model type:** Text Classification - **Language(s) (NLP):** More information needed - **License:** More information needed - **Parent Model:** BERT - **Resources for more information:** - GitHub Repo - Associated Paper - Blog Post The model should not be used to intentionally create hostile or alienating environments for people. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
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View details ↗Qwen3-30B-A3B-AWQ
ELVISIOThe AWQ version is quantized using ms-swift. You may refer to our best practice for training/fine-tuning Qwen3-models here. Note that the AWQ version for Qwen3-MoE models are verified to be working on Transformers/vLLM. We have not have the chance to tested them on other engines. The model has undergone AWQ int4 quantization using the ms-swift framework. Since the model is based on the MoE (Mixture of Experts) architecture, all `linear` layers except for `gate` and `lm_head` have been quantized.
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