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AI モデル 84759

• Gemma-4-Queen-31B-it-i1-GGUF↗

  key772003

• Qwen2.5-1.5B-Instruct-IMat-GGUF↗

  legraphista

• whiff-mamba2-50M-v0.1↗

  limloop

• bakLlava-v1-hf↗

  llava-hf

• Luqwen4-4B↗

  LLiserginov

• tiny-vit-random↗

  lysandre

• SpatialLM-Llama-1B↗

  manycore-research

  SpatialLM is a 3D large language model designed to process 3D point cloud data and generate structured 3D scene understanding outputs. These outputs include architectural elements like walls, doors, windows, and oriented object bounding boxes with their semantic categories. Unlike previous methods that require specialized equipment for data collection, SpatialLM can handle point clouds from diverse sources such as monocular video sequences, RGBD images, and LiDAR sensors. This multimodal architecture effectively bridges the gap between unstructured 3D geometric data and structured 3D representations, offering high-level semantic understanding. It enhances spatial reasoning capabilities for applications in embodied robotics, autonomous navigation, and other complex 3D scene analysis tasks. SpatialLM reconstructs 3D layout from a monocular RGB video with MASt3R-SLAM. Results aligned to video with GT cameras for visualization. In the current version of SpatialLM, input point clouds are considered axis-aligned where the z-axis is the up axis. This orientation is crucial for maintaining consistency in spatial understanding and scene interpretation across different datasets and applications. Example preprocessed point clouds, reconstructed from RGB videos using MASt3R-SLAM, are available in SpatialLM-Testset.

• Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF↗

  MaziyarPanahi

• phi-2↗

  Microsoft

  Phi-2 is a Transformer with **2.7 billion** parameters. It was trained using the same data sources as Phi-1.5, augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-2 showcased a nearly state-of-the-art performance among models with less than 13 billion parameters. Our model hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more. Phi-2 has been integrated in the `transformers` version 4.37.0, please ensure that you are using a version equal or higher than it.

• 5His2oaJ68NfPPN5VzXswzt2gdfscX43THB1DW9R9xjcLCKD_vgg↗

  minatolegend0308