sensenova/SenseNova-SI-1.1-Qwen3-VL-8B
SenseNova-SI-1.1-Qwen3-VL-8B is a multimodal foundation model developed by sensenova, built upon the Qwen3-VL architecture. This 8-billion parameter model is specifically designed to enhance spatial intelligence in multimodal tasks, leveraging a curated dataset of eight million diverse spatial data samples. It demonstrates strong performance across various spatial intelligence benchmarks while maintaining general multimodal understanding capabilities.
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SenseNova-SI-1.1-Qwen3-VL-8B: Enhanced Spatial Intelligence
SenseNova-SI-1.1-Qwen3-VL-8B is part of the SenseNova-SI family of multimodal foundation models, developed by sensenova, focusing on improving spatial intelligence. Built upon the Qwen3-VL architecture, this model addresses deficiencies in spatial reasoning often found in other multimodal models.
Key Capabilities & Features
- Specialized Spatial Intelligence: Trained on SenseNova-SI-8M, a dataset comprising eight million diverse spatial data samples, to cultivate robust spatial reasoning.
- Strong Benchmark Performance: Achieves notable scores on spatial intelligence benchmarks, including 64.8% on VSI, 38.1% on MMSI, 73.8% on MindCube-Tiny, 51.2% on ViewSpatial, and 49.6% on SITE.
- General Multimodal Understanding: Maintains strong general multimodal understanding, evidenced by 84.9% on MMBench-En.
- Open-source Compatibility: Built on popular open-source foundations to ensure maximum compatibility with existing research pipelines.
Ideal Use Cases
- Advanced Spatial Reasoning: Applications requiring precise understanding and interpretation of spatial relationships in visual data.
- Research in Multimodal AI: For researchers exploring emergent generalization capabilities and the impact of diverse data training in multimodal models.
- Benchmarking Spatial AI: As a strong baseline or comparison model for evaluating new spatial intelligence algorithms and datasets.