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AI 모델 84757

• 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

• Llama-3.1-Tulu-3-8B-RM↗

  allenai

• Olmo-3-1025-7B↗

  allenai

  We introduce Olmo 3, a new family of 7B and 32B models. This suite includes Base, Instruct, and Think variants. The Base models were trained using a staged training approach. Olmo is a series of **O**pen **l**anguage **mo**dels designed to enable the science of language models. These models are trained on the Dolma 3 dataset. We are releasing all code, checkpoints, and associated training details. The quantized model is more sensitive to data types and CUDA operations. To avoid potential issues, it's recommended to pass the inputs directly to CUDA using:

• Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized↗

  amkkk

• Parable-Granite-4.1-8B-Claude-Fable-5-GGUF↗

  AnkitAI

  > **~6 GB of RAM is all you need.** The Q4 build fits comfortably on an ordinary laptop or a mid-range GPU. > One command and you have a private, offline reasoning model on your machine: > > Parable-Granite-4.1-8B is an ibm-granite/granite-4.1-8b fine-tune trained on real multi-step agent sessions: planning, tool use, and ` ` reasoning captured from actual Claude Fable 5 and GPT-5.5 agent work, not synthetic Q&A. It is the largest release in the Parable series. **🔮 v2 is coming.** The 3B just got the v2 treatment (13× corpus, rebuilt recipe) — the same upgrade lands here next. Same links, in-place.

• Claude Opus 5.5 (Medium, Default Fallback)↗

  Anthropic

• T-lite-it-1.0-Q4_K_M-GGUF↗

  aovchinnikov