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AI Models 84759

• DeepSeek-V4.1-Flash-1017-EXL3-2.8bpw↗

  jayleaton

• Qwen3.8-3.6-27B-blend-GGUF↗

  JetBrains

  **Unofficial JetBrains derivative.** This model was prepared by JetBrains using Qwen models developed by Alibaba Cloud. This release is not affiliated with, sponsored by, or endorsed by Alibaba Cloud or Alibaba Group. *Tasks completed versus output tokens on JetBrains’ 100-task internal coding benchmark. Reasoning settings differ between configurations; see the article for methodology and context.* Read Making Local AI Smarter and Faster for the Junie Local release story, coding evaluations, token-efficiency results, and MTP runtime experiments.

• exaone-verireason-matched550-luna-fullft-e5-seed42↗

  Jongbin-kr

• LCO-Embedding-Omni-3B↗

  LCO-Embedding

• llm-compiler-7b-ftd-IMat-GGUF↗

  legraphista

• japanese-large-lm-3.6b-instruction-sft↗

  line-corporation

• Qwen2-Math-72B-Instruct-GGUF↗

  lmstudio-community

• Seed-OSS-36B-Instruct-MLX-8bit↗

  lmstudio-community

  _👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: ByteDance-Seed **Original model**: Seed-OSS-36B-Instruct **MLX quantization**: provided by LM Studio team using mlx_lm 8-bit quantized version of Seed-OSS-36B-Instruct using MLX, optimized for Apple Silicon.

• superpoint↗

  magic-leap-community

  The SuperPoint model was proposed in SuperPoint: Self-Supervised Interest Point Detection and Description by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. This model is the result of a self-supervised training of a fully-convolutional network for interest point detection and description. The model is able to detect interest points that are repeatable under homographic transformations and provide a descriptor for each point. The use of the model in its own is limited, but it can be used as a feature extractor for other tasks such as homography estimation, image matching, etc. *This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.*

• Llama-3-8B-Magpie-Align-SFT-v0.3↗

  Magpie-Align

  This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on - Magpie-Align/Magpie-Pro-MT-300K-v0.1, - Magpie-Align/Magpie-Reasoning-150K, and - Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese Compared to v0.2, we enhance its multi-lingual ability by incorporating a new dataset with 200K Chinese instructions. It achieves performance comparable with the official Llama-3-8B-Instruct Model **with SFT only**! The detailed benchmark performance is as follows: **How to use it?** Please check the official Llama 3 repository for detailed instructions. Simply replace the original `model_id` with `Magpie-Align/Llama-3-8B-Magpie-Align-SFT-v0.3`.