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AI 모델 84759

• gpt-oss-20b-BF16-w4a16-llmcompressor↗

  amd

• Baichuan-13B-Base↗

  baichuan-inc

• Lyra_Gutenbergs-Twilight_Magnum-12B-GGUF↗

  bartowski

• ReadyArt_Amoral-Fallen-Omega-Gemma3-12B-GGUF↗

  bartowski

• BugTraceAI-CORE-Fast↗

  BugTraceAI

• bert-base-arabic-camelbert-msa-ner↗

  CAMeL-Lab

  **CAMeLBERT MSA NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Modern Standard Arabic (MSA) model. For the fine-tuning, we used the ANERcorp dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models. "* Our fine-tuning code can be found here. You can use the CAMeLBERT MSA NER model directly as part of our CAMeL Tools NER component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools NER component:

• Gemma-4-E4B-it-Uncensored-GGUF↗

  ccharnkij

• RavenX-Sec-8B-GGUF↗

  deadbydawn101

• GLM-5.3-ABLITERATED-NVFP4↗

  dealignai

• Depth-Anything-V2-Metric-Outdoor-Small-hf↗

  depth-anything

  This model represents a fine-tuned version of Depth Anything V2 for outdoor metric depth estimation using the synthetic Virtual KITTI datasets. Depth Anything V2 was introduced in the paper of the same name by Lihe Yang et al. It uses the same architecture as the original Depth Anything release but employs synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. This fine-tuned version for metric depth estimation was first released in this repository. **Six metric depth models** of three scales for indoor and outdoor scenes, respectively, were released and are available: