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AI 모델 84759

• classifier-rest-0.1↗

  absa

• DeepSeek-R1-0528-MXFP4-MTP-MoEFP4↗

  amd

• Ateron_Gemma-4-Dark-Thoughts-31B-GGUF↗

  bartowski

  **Model details:** - Parameter count: 31B - Input support: text, image and audio (with mmproj file) - details - MTP: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (19.60GB) - usually a good mix of size and performance. Download instructions available here The files marked `true` in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

• Llama-3-Hercules-5.1-8B-GGUF↗

  bartowski

• QwQ-LCoT-7B-Instruct-GGUF↗

  bartowski

• Skywork-o1-Open-Llama-3.1-8B-GGUF↗

  bartowski

• FLUX.1-Canny-dev-lora↗

  Black Forest Labs

• distilbert-base-japanese↗

  BNKEN

• Qwen-Image-2512-GGUF↗

  Civitai

• FastPDN↗

  clarin-pl

  FastPolDeepNer is model for Named Entity Recognition, designed for easy use, training and configuration. The forerunner of this project is PolDeepNer2. The model implements a pipeline consisting of data processing and training using: hydra, pytorch, pytorch-lightning, transformers. The FastPDN model was trained on datasets (with 82 class versions) of kpwr and cen. Annotation guidelines are specified here. FastPDN models have been fine-tuned, thanks to pretrained models: - herbert-base-case - distiluse-base-multilingual-cased-v1