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次のアイデアに合うAIを見つけましょう。
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。
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。
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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。
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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提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。
