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AI 模型 84759

• Aurora-One-Mini↗

  North-ML1

• Cosmos-UpsamplePrompt1-12B-Text2World↗

  NVIDIA

• whisper-base_timestamped↗

  onnx-community

  https://huggingface.co/openai/whisper-base with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

• diffusers-cd_cat256_l2↗

  OpenAI

• EVisRAG-7B↗

  openbmb

• MiniCPM-V-4_5-int4↗

  openbmb

  **MiniCPM-V 4.5** is the latest and most capable model in the MiniCPM-V series. The model is built on Qwen3-8B and SigLIP2-400M with a total of 8B parameters. It exhibits a significant performance improvement over previous MiniCPM-V and MiniCPM-o models, and introduces new useful features. Notable features of MiniCPM-V 4.5 include: Both Video-MME and OpenCompass were evaluated using 8×A100 GPUs for inference. The reported inference time of Video-MME includes full model-side computation, and excludes the external cost of video frame extraction (dependent on specific frame extraction tools) for fair comparison. We deploy MiniCPM-V 4.5 on iPad M4 with iOS demo. The demo video is the raw screen recording without editing.

• Boris-125M↗

  opencerebral

• InternVL3_5-4B-Pretrained↗

  OpenGVLab

• tiny-random-HubertModel↗

  optimum-intel-internal-testing

• PaddleOCR-VL-1.6-GGUF↗

  PaddlePaddle

  PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training **🔥 Official Website** **📝 Technical Report** We introduce PaddleOCR-VL-1.6, an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. PaddleOCR-VL-1.6 introduces a region-aware data optimization framework that identifies weak regions from the previous model, applies targeted enhancement to those regions, and improves the reliability of supervision signals. It further adopts a progressive post-training recipe based on curated data selection and reinforcement learning, pushing model performance to a higher level through staged optimization. **PaddleOCR-VL-1.6 achieves a new state-of-the-art score of 96.33% on OmniDocBench v1.6, sets new records on OmniDocBench v1.5 and Real5-OmniDocBench as well**, and demonstrates strong competitiveness against top-tier VLMs. The model architecture is fully compatible with PaddleOCR-VL-1.5, enabling zero-cost plug-and-play migration.