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AI Models 85003

• Qwimi-3.6-27B-Coder-MTP-GGUF↗

  trjxter

• tiny-Idefics3ForConditionalGeneration↗

  trl-internal-testing

• ARK-270M-Base-GGUF↗

  usermma

• flexray↗

  VictorButoi

  FleXray is a single 2D UNet that segments anatomy from standard radiographs across body regions, projections, and acquisition settings. It predicts 60 anatomical structures (plus background) as independent sigmoid channels at 256 x 256 resolution. This repository holds the **flagship model** and the four sibling models of the FleXray ensemble, one bundle per `members/` subfolder (see Repository layout). `flexify` and `FleXraySegmenter.from_pretrained` load the flagship by default. `flexify` writes ` _masks.npy`, ` _probabilities.npy`, and ` _logits.npy` per image, each shaped `CxHxW`. The Python API keeps the batch dimension (`B=1` for a single image). Channel order follows `label_schema.json`. Pass `--binary LABEL` (for example `--binary femurs`) to write one label. See docs/inference.md for the full CLI and Python API.

• SSVAE↗

  zai-org

• visualglm-6b↗

  zai-org

• c10↗

  zeras141a

• efficientloftr↗

  zju-community

  The Efficient LoFTR model was proposed in "Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed" by Yifan Wang, Xingyi He, Sida Peng, Dongli Tan, and Xiaowei Zhou from Zhejiang University. This model presents a novel method for efficiently producing semi-dense matches across images, addressing the limitations of previous detector-free matchers like LoFTR, which suffered from low efficiency despite remarkable matching capabilities in challenging scenarios. Efficient LoFTR revisits design choices to improve both efficiency and accuracy. "We present a novel method for efficiently producing semi-dense matches across images. Previous detector-free matcher LOFTR has shown remarkable matching capability in handling large-viewpoint change and texture-poor scenarios but suffers from low efficiency. We revisit its design choices and derive multiple improvements for both efficiency and accuracy. One key observation is that performing the transformer over the entire feature map is redundant due to shared local information, therefore we propose an aggregated attention mechanism with adaptive token selection for efficiency. Furthermore, we find spatial variance exists in LoFTR's fine correlation module, which is adverse to matching accuracy. A novel two-stage correlation layer is proposed to achieve accurate subpixel correspondences for accuracy improvement. Our efficiency optimized model is ~2.5 faster than LoFTR which can even surpass state-of-the-art efficient sparse matching pipeline SuperPoint + LightGlue. Moreover, extensive experiments show that our method can achieve higher accuracy compared with competitive semi-dense matchers, with considerable efficiency benefits. This opens up exciting prospects for large-scale or latency-sensitive applications such as image retrieval and 3D reconstruction. Project page: https://zju3dv.github.io/efficientloftr/"

• spark-s1-4b-v8-GGUF↗

  abhishek085

• occamy-1.0-MLX-6bit↗

  Accio-Lab