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AI Models 84760

• vectionlabs_Salience-27B-R5-GGUF↗

  bartowski

  **Model details:** - Parameter count: 28B - Input support: text, image (with mmproj file) - details - Speculative decoding: yes (MTP) - details - imatrix: yes - details - Perplexity/KLD measured: no **Don't know which to choose?** Grab Q4_K_M (17.77GB) - 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:

• SRPO-GGUF↗

  befox

• Orthrus-Qwen3-1.7B↗

  chiennv

• banglat5↗

  csebuetnlp

• embeddinggemma-300m-GGUF↗

  cstr

• nemotron-3.5-asr-streaming-GGUF↗

  cstr

• 260828_OhLoRA_LLM↗

  daebakgazua

• MiniMax-M2.7-GGUF↗

  dennny123

• Depth-Anything-V2-Large↗

  depth-anything

  Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model with the following features: - more fine-grained details than Depth Anything V1 - more robust than Depth Anything V1 and SD-based models (e.g., Marigold, Geowizard) - more efficient (10x faster) and more lightweight than SD-based models - impressive fine-tuned performance with our pre-trained models Download the model first and put it under the `checkpoints` directory. ```bibtex @article{depth_anything_v2, title={Depth Anything V2}, author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang}, journal={arXiv:2406.09414}, year={2024} }

• mms-tts-csk↗

  facebook