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This model is an int4 model with group_size 128 and symmetric quantization of Qwen/Qwen2-0.5B-Instruct generated by intel/auto-round algorithm. ⚠️ Important: This model is used for internal testing with VLLM. Please do not delete or modify without approval.
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This model is an int4 model with group_size 128 of Qwen/Qwen3.5-122B-A10B generated by intel/auto-round. Please follow the license of the original model. **The main branch provides the tuned quantized model, and revision 3f4ba63 corresponds to the RTN version. In general, the tuned version is preferred; however, it has not been validated for this particular model.** ~~~bash vllm serve Intel/Qwen3.5-122B-A10B-int4-AutoRound --port 8000 --tensor-parallel-size 1 --max-model-len 2048 --reasoning-parser qwen3 --served-model-name qwen ~~~
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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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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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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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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。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
This model is a mixed int4 model with group_size 128 of Qwen/Qwen3.6-35B-A3B generated by intel/auto-round. Please follow the license of the original model. **Some users have reported an infinite loop issue in our INT4 version. We have added fallbacks for certain layers in this release, but it is still unclear whether the issue has been fully resolved. Please use it with caution.** The main branch is generated by `auto-round-best` and revision 54e7cd36d9f7a358b4b42740c3aef755638e5ea6 is generated by `auto-round`. Tipically `auto-round-best` is better
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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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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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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。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
This model is an int4 model with group_size 128 and symmetric quantization of Qwen/Qwen3-VL-30B-A3B-Instruct generated by intel/auto-round. Please refer to Section Generate the model for more details. Please follow the license of the original model. The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing.
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The original source of this model is : runwayml/stable-diffusion-v1-5. This model is just optimized and converted to Intermediate Representation (IR) using OpenVino's Model Optimizer and POT tool to run on Intel's Hardware - CPU, GPU, NPU. We have FP16 and INT8 versions of the model. Please note currently only unet model is quantized to int8. Intended to be used with: - GIMP plugin openvino-ai-plugins-gimp - Blender Addon dream-textures-openvino
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This is useful for functional testing (not quality generation, since its weights are random) on optimum-intel
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The TVP model was proposed in Text-Visual Prompting for Efficient 2D Temporal Video Grounding by Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding. The goal of this model is to incorporate trainable prompts into both visual inputs and textual features to temporal video grounding(TVG) problems. It was introduced in this paper. Relevant factors: The model's performance may vary across different video content, such as variations in video quality, lighting conditions, or genres (e.g., action vs. dialogue-heavy scenes). Evaluation factors: Performance has been evaluated on benchmark datasets like Charades-STA and ActivityNet Captions, focusing on metrics relevant to temporal video grounding accuracy. Model performance measures: The model employs metrics such as the Temporal-Distance IoU (TDIoU) loss for efficient learning and performance evaluation in TVG tasks.
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ZoeDepth model fine-tuned on the NYU dataset. It was introduced in the paper ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth by Shariq et al. and first released in this repository. ZoeDepth extends the DPT framework for metric (also called absolute) depth estimation, obtaining state-of-the-art results. Disclaimer: The team releasing ZoeDepth did not write a model card for this model so this model card has been written by the Hugging Face team.
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可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
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。
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。
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。
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。
