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Explore AI models 85003
paecter
mpi-inno-compPaECTER (Patent Embeddings using Citationinformed TransformERs) is a patent similarity model. Built upon Google's BERT for Patents as its base model, it generates 1024-dimensional dense vector embeddings from patent text. These vectors encapsulate the semantic essence of the given patent text, making it highly suitable for various downstream tasks related to patent analysis. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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View details ↗GUI-Owl 1.5 is the next-generation native GUI agent model family built on Qwen3-VL. It supports multi-platform GUI automation across desktops, mobile devices, browsers, and more. Powered by a scalable hybrid data flywheel, unified agent capability enhancement, and multi-platform environment RL (MRPO), GUI-Owl 1.5 offers a full spectrum of models. **Key highlights:** - 🏆 **State-of-the-art** among multi-platform GUI models on OSWorld-Verified, AndroidWorld, Mobile-World, WindowsAA, ScreenSpot-v2, ScreenSpot-Pro, and more. - 🔧 **Tool & MCP calling**: Native support for external tool invocation and MCP server coordination, achieving top performance on OSWorld-MCP and Mobile-World. - 🧠 **Long-horizon memory**: Built-in memory capability without external workflow orchestration, leading all native agent models on MemGUI-Bench. - 🤝 **Multi-agent ready**: Serves both as a standalone end-to-end agent and as specialized roles (planner, executor, verifier, notetaker) within the Mobile-Agent-v3.5 framework. - ⚡ **Instruct & Thinking variants**: Smaller instruct models for fast inference and edge deployment; larger thinking models for complex tasks requiring planning and reflection. Please refer to the technical report for detailed results on ScreenSpot-v2, ScreenSpot-Pro, OSWorld-G, MMBench-GUI, and more.
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View details ↗GUI-Owl-1.5-8B-Think
mPLUGGUI-Owl 1.5 is the next-generation native GUI agent model family built on Qwen3-VL. It supports multi-platform GUI automation across desktops, mobile devices, browsers, and more. Powered by a scalable hybrid data flywheel, unified agent capability enhancement, and multi-platform environment RL (MRPO), GUI-Owl 1.5 offers a full spectrum of models. * **Paper**: https://arxiv.org/abs/2602.16855 * **GitHub Repository**: https://github.com/X-PLUG/MobileAgent * **Online Demo**: http://modelscope.cn/studios/MobileAgentTest/computer_use **Key highlights:** - 🏆 **State-of-the-art** among multi-platform GUI models on OSWorld-Verified, AndroidWorld, Mobile-World, WindowsAA, ScreenSpot-v2, ScreenSpot-Pro, and more. - 🔧 **Tool & MCP calling**: Native support for external tool invocation and MCP server coordination, achieving top performance on OSWorld-MCP and Mobile-World. - 🧠 **Long-horizon memory**: Built-in memory capability without external workflow orchestration, leading all native agent models on MemGUI-Bench. - 🤝 **Multi-agent ready**: Serves both as a standalone end-to-end agent and as specialized roles (planner, executor, verifier, notetaker) within the Mobile-Agent-v3.5 framework. - ⚡ **Instruct & Thinking variants**: Smaller instruct models for fast inference and edge deployment; larger thinking models for complex tasks requiring planning and reflection.
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View details ↗GUI-Owl-7B
mPLUG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 ↗mPLUG-Owl3-7B-240728
mPLUG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 ↗TinyChart-3B-768
mPLUG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 ↗Flux_Lustly.ai_Uncensored_nsfw_v1
Mpowers963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 ↗This ASR model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on Turkish language. The following datasets were used for finetuning: - Common Voice 7.0 TR All `validated` split except `test` split was used for training. - MediaSpeech To support both of the datasets above, custom pre-processing and loading steps was performed and wav2vec2-turkish repo was used for that purpose.
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View details ↗The following datasets were used for finetuning: - Common Voice 8.0 TR All `validated` split except `test` split was used for training. To support the datasets above, custom pre-processing and loading steps was performed and wav2vec2-turkish repo was used for that purpose. The following hypermaters were used for finetuning: - learning_rate 2.5e-4 - num_train_epochs 20 - warmup_steps 500 - freeze_feature_extractor - mask_time_prob 0.1 - mask_feature_prob 0.1 - feat_proj_dropout 0.05 - attention_dropout 0.05 - final_dropout 0.1 - activation_dropout 0.05 - per_device_train_batch_size 8 - per_device_eval_batch_size 8 - gradient_accumulation_steps 8
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View details ↗csm-1b-arabic
mqqz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 ↗smart-community-ai
Mr-Itachi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 ↗dit-Q4_0-Qualcomm
Mr-J-3691. Install FancyAI and open **Aura → Get Models** 2. Download the **components** row for your model's family (encoder + VAE, one tap, only once) 3. Download any model file from this repo to your phone (e.g. `Download/`) 4. Back in the app: **Read from disk** → pick the file → render
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View details ↗KleidiAi_support
Mr-J-369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 ↗sonda-1.0-4B
Mr-Shmoo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 ↗BFS
mr2along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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