Qwen2.5-7B-Instruct-Desyced
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View details ↗DISCOVER & COMPARE
Explore AI models 84956
Qwen2.5-7B-Parasite
ApolloRaines基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen2.5-Coder-14B-Instruct-Jbliterated
ApolloRaines基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen2.5-Coder-32B-Instruct-Jbliterated
ApolloRaines基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen2.5-Coder-7B-Instruct-MLX
apolloransom基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Cosmos-Reason2-2B-GGUF
apolo13xNVIDIA Cosmos Reason 2 is an open, 2B-parameter reasoning vision-language model (VLM) for physical AI and robotics. It is post-trained from Qwen3-VL-2B-Instruct and understands space, time, and fundamental physics. **Key capabilities:** - Physical AI reasoning with spatio-temporal understanding - Object detection with 2D/3D point localization and bounding boxes - Long-context understanding up to 256K input tokens - Video analytics, data curation, and robot planning
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View details ↗Cosmos-Reason2-8B-GGUF
apolo13x基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.5-27B-NVFP4
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View details ↗Qwen3.5-27B-quantized.w4a16
apolo13x基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.5-35B-A3B-NVFP4
apolo13x基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.5-35B-A3B-quantized.w4a16
apolo13x基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.5-9B-NVFP4
apolo13x基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗fishaudio-s2-pro-8bit-mlx
appautomaton基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗urdu-llm-model
appdeveloper512基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗urdu-llm-model-v3
appdeveloper512基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗We introduce the AIMv2 family of vision models pre-trained with a multimodal autoregressive objective. AIMv2 pre-training is simple and straightforward to train and to scale effectively. Some AIMv2 highlights include: 1. Outperforms OAI CLIP and SigLIP on the majority of multimodal understanding benchmarks. 2. Outperforms DINOv2 on open-vocabulary object detection and referring expression comprehension. 3. Exhibits strong recognition performance with AIMv2-3B achieving *89.5% on ImageNet using a frozen trunk*.
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View details ↗Depth Anything V2 was introduced in the paper of the same name by Lihe Yang et al. It uses the same architecture as the original Depth Anything release, but uses synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. The original Depth Anything model was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al., and was first released in this repository. Depth Anything V2 leverages the DPT architecture with a DINOv2 backbone. The model is trained on ~600K synthetic labeled images and ~62 million real unlabeled images, obtaining state-of-the-art results for both relative and absolute depth estimation.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗coreml-mobileclip
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View details ↗coreml-sam2.1-large
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View details ↗coreml-sam2.1-small
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