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AI モデル 84759

• QI-neural-chat-7B-ko-DPO-GGUF↗

  joongi007

• boreal-flux-dev-v2↗

  kudzueye

• Spark-X2.5-GGUF↗

  lainlives

• bilingual-embedding-small↗

  Lajavaness

  Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of Multilingual-MiniLM-L12-H384, a pre-trained language model is built upon multilingual-e5 architecture. The model utilizes MiniLM to encode english-french sentences into a 384-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language. @article{conneau2019unsupervised, title={Unsupervised cross-lingual representation learning at scale}, author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1911.02116}, year={2019} } @article{reimers2019sentence, title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, author={Nils Reimers, Iryna Gurevych}, journal={https://arxiv.org/abs/1908.10084}, year={2019} }

• legal-roberta-large↗

  lexlms

• K-EXAONE-236B-A23B-GGUF↗

  LGAI-EXAONE

• llm-jp-3-1.8b-instruct↗

  llm-jp

• MS3.2-PaintedFantasy-v4.1-24B-ultra-uncensored-heretic-v1-GGUF↗

  llmfan46

• gemma-3n-E4B-it-MLX-bf16↗

  lmstudio-community

  *👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator:** google **Original model**: gemma-3n-E4B-it **MLX quantization:** provided by LM Studio team using mlx_vlm Original bfloat16 version of gemma-3n-E4B-it using MLX, optimized for Apple Silicon.

• Talk2DINOv3-ViTB↗

  lorebianchi98

  Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation (ICCV 2025) Luca Barsellotti*   Lorenzo Bianchi*   Nicola Messina   Fabio Carrara   Marcella Cornia   Lorenzo Baraldi   Fabrizio Falchi   Rita Cucchiara Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers, they face challenges in spatial localization due to their global alignment of image and text features. Conversely, self-supervised visual models like DINO excel in fine-grained visual encoding but lack integration with language. To bridge this gap, we present Talk2DINO, a novel hybrid approach that combines the spatial accuracy of DINOv2 with the language understanding of CLIP. Our approach aligns the textual embeddings of CLIP to the patch-level features of DINOv2 through a learned mapping function without the need to fine-tune the underlying backbones. At training time, we exploit the attention maps of DINOv2 to selectively align local visual patches with textual embeddings. We show that the powerful semantic and localization abilities of Talk2DINO can enhance the segmentation process, resulting in more natural and less noisy segmentations, and that our approach can also effectively distinguish foreground objects from the background. Experimental results demonstrate that Talk2DINO achieves state-of-the-art performance across several unsupervised OVS benchmarks.