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AI モデル 84759

• paligemma-3b-ft-ocrvqa-224-jax↗

  Google

• siglip-base-patch16-256-multilingual↗

  Google

  SigLIP model pre-trained on WebLi at resolution 256x256. It was introduced in the paper Sigmoid Loss for Language Image Pre-Training by Zhai et al. and first released in this repository. Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team. SigLIP is CLIP, a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. This allows further scaling up the batch size, while also performing better at smaller batch sizes.

• SinhalaLM-V.0.0.1↗

  HelaAI

• shingi-27b↗

  kortexa-ai

• Krea-2-LoRA-rainywindow↗

  krea

• PAW-24B-X3.1↗

  lackonendes

• BUD-E-Whisper↗

  laion

• anysplat↗

  lhjiang

  See the Github repository: https://github.com/OpenRobotLab/AnySplat regarding installation instructions.

• LightOnOCR-2-1B-base↗

  lightonai

  **Base model for fine-tuning.** This is the pre-RLVR checkpoint with strong OCR capabilities, ideal as a starting point for domain adaptation and custom fine-tuning. LightOnOCR-2 is an efficient end-to-end 1B-parameter vision-language model for converting documents (PDFs, scans, images) into clean, naturally ordered text without relying on brittle pipelines. This second version is trained on a larger and higher-quality corpus with stronger French, arXiv, and scan coverage, improved LaTeX handling, and cleaner normalization. LightOnOCR-2 achieves state-of-the-art performance on OlmOCR-Bench while being ~9× smaller and significantly faster than competing approaches.

• ShareGPT4V-7B↗

  Lin-Chen