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AI 模型 84760

• Qwen2.5-Coder-0.5B-Instruct-GGUF↗

  QuantFactory

• Zoom-Call↗

  Remade-AI

• BSC-LT_-_salamandra-7b-gguf↗

  RichardErkhov

• nbeerbower_-_mistral-nemo-wissenschaft-12B-gguf↗

  RichardErkhov

• nota-ai_-_st-vicuna-v1.3-10.5b-taylor-gguf↗

  RichardErkhov

• progressive_rock-acestep1.5-xl-v1↗

  ryanontheinside

• Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF↗

  Sanio7791

• distiluse-base-multilingual-cased-v2↗

  sentence-transformers

  This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

• oneformer_ade20k_dinat_large↗

  shi-labs

  OneFormer model trained on the ADE20k dataset (large-sized version, Dinat backbone). It was introduced in the paper OneFormer: One Transformer to Rule Universal Image Segmentation by Jain et al. and first released in this repository. OneFormer is the first multi-task universal image segmentation framework. It needs to be trained only once with a single universal architecture, a single model, and on a single dataset, to outperform existing specialized models across semantic, instance, and panoptic segmentation tasks. OneFormer uses a task token to condition the model on the task in focus, making the architecture task-guided for training, and task-dynamic for inference, all with a single model. You can use this particular checkpoint for semantic, instance and panoptic segmentation. See the model hub to look for other fine-tuned versions on a different dataset.

• goLLeM-110M-PL-SFT-merged↗

  SlayerLab