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Model: SenseNova/SenseNova-SI-1.1-Qwen2.5-VL-3B Source: Original Platform
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---
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-VL-3B-Instruct
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pipeline_tag: image-text-to-text
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library_name: transformers
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---
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**EN** | [中文](README_CN.md)
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# SenseNova-SI: Scaling Spatial Intelligence with Multimodal Foundation Models
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<a href="https://github.com/OpenSenseNova/SenseNova-SI" target="_blank">
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<img alt="Code" src="https://img.shields.io/badge/SenseNova_SI-Code-100000?style=flat-square&logo=github&logoColor=white" height="20" />
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</a>
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<a href="https://arxiv.org/abs/2511.13719" target="_blank">
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<img alt="arXiv" src="https://img.shields.io/badge/arXiv-SenseNova_SI-red?logo=arxiv" height="20" />
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</a>
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<a href="https://github.com/EvolvingLMMs-Lab/EASI" target="_blank">
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<img alt="Code" src="https://img.shields.io/badge/EASI-Code-100000?style=flat-square&logo=github&logoColor=white" height="20" />
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</a>
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<a href="https://easi.lmms-lab.com/leaderboard" target="_blank">
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<img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_EASI-Leaderboard-ffc107?color=ffc107&logoColor=white" height="20" />
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</a>
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## Overview
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Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence.
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In this work, we explore scaling up multimodal foundation models to cultivate spatial intelligence within the **SenseNova-SI family**,
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built upon established multimodal foundations including visual understanding models (i.e., Qwen3-VL and InternVL3) and unified understanding and generation models (i.e., Bagel).
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We take a principled approach to constructing high-performing and robust spatial intelligence by systematically curating SenseNova-SI-8M:
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eight million diverse data samples under a rigorous taxonomy of spatial capabilities.
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SenseNova-SI demonstrates unprecedented performance across a broad range of spatial intelligence benchmarks: 68.7% on VSI-Bench, 43.3% on MMSI, 85.6% on MindCube,
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54.6% on ViewSpatial, and 50.1% on SITE, while maintaining strong general multimodal understanding (e.g., 84.9% on MMBench-En).
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More importantly, we analyze the impact of data scaling, discuss early signs of emergent generalization capabilities enabled by diverse data training,
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analyze the risk of overfitting and language shortcuts, present a preliminary study on spatial chain-of-thought reasoning, and validate the potential downstream application. SenseNova-SI is an ongoing project, and this report will be updated continuously.
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All newly trained multimodal foundation models are publicly released to facilitate further research in this direction.
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*In the future, SenseNova-SI will be integrated with larger-scale in-house models.*
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## Release Information
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Currently, we build SenseNova-SI upon popular open-source foundation models to maximize compatibility with existing research pipelines.
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In this release, we present
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[**SenseNova-SI-1.2-InternVL3-8B**](https://huggingface.co/sensenova/SenseNova-SI-1.2-InternVL3-8B), [**SenseNova-SI-1.1-Qwen2.5-VL-3B**](https://huggingface.co/sensenova/SenseNova-SI-1.1-Qwen2.5-VL-3B), [**SenseNova-SI-1.1-Qwen2.5-VL-7B**](https://huggingface.co/sensenova/SenseNova-SI-1.1-Qwen2.5-VL-7B), and [**SenseNova-SI-1.1-Qwen3-VL-8B**](https://huggingface.co/sensenova/SenseNova-SI-1.1-Qwen3-VL-8B), of which **SenseNova-SI-1.2-InternVL3-8B** achieve state-of-the-art performance among open-source models of comparable size across eight recent spatial intelligence benchmarks:
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**VSI**, **MMSI**, **MindCube**, **ViewSpatial**, **SITE**, **BLINK**, **3DSRBench**, **EmbSpatial-Bench**.
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<table>
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<thead>
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<tr>
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<th>Model</th>
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<th>VSI</th>
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<th>MMSI</th>
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<th>MindCube-Tiny</th>
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<th>ViewSpatial</th>
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<th>SITE</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td colspan="6" align="center"><em>Open-source Models (~2B)</em></td>
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</tr>
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<tr>
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<td>InternVL3-2B</td><td>32.9</td><td>26.5</td><td>37.5</td><td>32.5</td><td>30.0</td>
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</tr>
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<tr>
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<td>Qwen2.5-VL-3B-Instruct</td><td>27.0</td><td>28.6</td><td>37.6</td><td>31.9</td><td>33.1</td>
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</tr>
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<tr>
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<td>Qwen3-VL-2B-Instruct</td><td>50.3</td><td>28.9</td><td>34.5</td><td>36.9</td><td>35.6</td>
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</tr>
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<tr>
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<td>MindCube-3B-RawQA-SFT</td><td>17.2</td><td>1.7</td><td>51.7</td><td>24.1</td><td>6.3</td>
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</tr>
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<tr>
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<td>SpatialLadder-3B</td><td>44.8</td><td>27.4</td><td>43.4</td><td>39.8</td><td>27.9</td>
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</tr>
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<tr>
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<td>SpatialMLLM-4B</td><td>46.3</td><td>26.1</td><td>33.4</td><td>34.6</td><td>18.0</td>
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</tr>
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<tr>
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<td>VST-3B-SFT</td><td><strong>57.9</strong></td><td>30.2</td><td>35.9</td><td><strong>52.8</strong></td><td>35.8</td>
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</tr>
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<tr>
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<td>Cambrian-S-3B</td><td>57.3</td><td>25.2</td><td>32.5</td><td>39.0</td><td>28.3</td>
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</tr>
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<tr>
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<td><strong>SenseNova-SI-1.1-Qwen2.5-VL-3B</strong></td>
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<td>54.9</td>
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<td><strong>30.8</strong></td>
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<td><strong>52.6</strong></td>
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<td>43.5</td>
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<td><strong>37.8</strong></td>
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</tr>
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<tr>
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<td colspan="6" align="center"><em>Proprietary Models</em></td>
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</tr>
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<tr>
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<td>Gemini-2.5-pro-2025-06</td><td>53.5</td><td>38.0</td><td>57.6</td><td>46.0</td><td>57.0</td>
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</tr>
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<tr>
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<td>Grok-4-2025-07-09</td><td>47.9</td><td>37.8</td><td>63.5</td><td>43.2</td><td>47.0</td>
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</tr>
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<tr>
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<td>GPT-5-2025-08-07</td><td>55.0</td><td>41.8</td><td>56.3</td><td>45.5</td><td>61.8</td>
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</tr>
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</tbody>
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</table>
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## Evaluation
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To reproduce the benchmark results above, please refer to [EASI](https://easi.lmms-lab.com/leaderboard/) to evaluate SenseNova-SI on mainstream spatial intelligence benchmarks.
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## Citation
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```bib
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@InProceedings{sensenova-si,
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title = {Scaling Spatial Intelligence with Multimodal Foundation Models},
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author = {Cai, Zhongang and Wang, Ruisi and Gu, Chenyang and Pu, Fanyi and Xu, Junxiang and Wang, Yubo and Yin, Wanqi and Yang, Zhitao and Wei, Chen and Sun, Qingping and Zhou, Tongxi and Li, Jiaqi and Pang, Hui En and Qian, Oscar and Wei, Yukun and Lin, Zhiqian and Shi, Xuanke and Deng, Kewang and Han, Xiaoyang and Chen, Zukai and Fan, Xiangyu and Deng, Hanming and Lu, Lewei and Pan, Liang and Li, Bo and Liu, Ziwei and Wang, Quan and Lin, Dahua and Yang, Lei},
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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year = {2026}
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}
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```
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99
README_CN.md
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[EN](README.md) | **中文**
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# SenseNova-SI: 探索空间智能在多模态基础模型上尺度效应
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<a href="https://github.com/OpenSenseNova/SenseNova-SI" target="_blank">
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<img alt="Code" src="https://img.shields.io/badge/SenseNova_SI-Code-100000?style=flat-square&logo=github&logoColor=white" height="20" />
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</a>
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<a href="https://arxiv.org/abs/2511.13719" target="_blank">
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<img alt="arXiv" src="https://img.shields.io/badge/arXiv-SenseNova_SI-red?logo=arxiv" height="20" />
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</a>
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<a href="https://github.com/EvolvingLMMs-Lab/EASI" target="_blank">
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<img alt="Code" src="https://img.shields.io/badge/EASI-Code-100000?style=flat-square&logo=github&logoColor=white" height="20" />
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</a>
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<a href="https://easi.lmms-lab.com/leaderboard" target="_blank">
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<img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_EASI-Leaderboard-ffc107?color=ffc107&logoColor=white" height="20" />
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</a>
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## 概览
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尽管多模态基础模型已取得显著进展，但在空间智能方面仍存在明显不足。
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本研究基于成熟的多模态基础，包括视觉理解模型（如Qwen3-VL、InternVL3）和统一理解生成模型（如Bagel），从尺度效应（Scaling）的视角构建了**SenseNova-SI系列模型**。
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我们采用系统化方法构建了包含800万样本的SenseNova-SI-8M数据集，通过严格的空间能力分类体系培养高性能、高鲁棒性的空间能力。
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该系列模型在多项空间智能基准测试中取得突破性表现：VSI-Bench 68.7%、MMSI 43.3%、MindCube 85.6%、ViewSpatial 54.6%、SITE 50.1%，同时保持强大的通用多模态理解能力（如MMBench-En 84.9%）。
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本研究进一步分析了数据规模的影响，揭示了多样化数据训练带来的涌现泛化能力，探讨了过拟合与语言捷径的风险，提出了空间思维链推理的初步研究，并验证了下游应用潜力。
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SenseNova-SI是一个持续迭代的项目，所有新训练的多模态空间智能基础模型均将陆续开源，以推动空间智能领域的研究发展。
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*后续 SenseNova-SI 将与更大规模的内部模型进行集成。*
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## 发布信息
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目前，我们基于流行的开源基础模型构建 SenseNova-SI，以最大化与现有研究流程的兼容性。
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在本次发布中，我们推出
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[**SenseNova-SI-1.2-InternVL3-8B**](https://huggingface.co/sensenova/SenseNova-SI-1.2-InternVL3-8B),
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[**SenseNova-SI-1.1-Qwen2.5-VL-3B**](https://huggingface.co/sensenova/SenseNova-SI-1.1-Qwen2.5-VL-3B),
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[**SenseNova-SI-1.1-Qwen2.5-VL-7B**](https://huggingface.co/sensenova/SenseNova-SI-1.1-Qwen2.5-VL-7B),
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与[**SenseNova-SI-1.1-Qwen3-VL-8B**](https://huggingface.co/sensenova/SenseNova-SI-1.1-Qwen3-VL-8B)，
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其中**SenseNova-SI-1.2-InternVL3-8B**在八个近期发布的空间智能基准测试（**VSI**、**MMSI**、**MindCube**、**ViewSpatial**、**SITE**、**BLINK**、**3DSRBench**、**EmbSpatial-Bench**）上，
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在同等模型规模下均取得了开源模型的最新最优性能（state-of-the-art）。
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<table>
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<thead>
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<tr>
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<th>Model</th>
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<th>VSI</th>
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<th>MMSI</th>
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<th>MindCube-Tiny</th>
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<th>ViewSpatial</th>
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<th>SITE</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td colspan="6" align="center"><em>Open-source Models (~2B)</em></td>
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</tr>
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<tr>
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<td>InternVL3-2B</td><td>32.9</td><td>26.5</td><td>37.5</td><td>32.5</td><td>30.0</td>
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</tr>
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<tr>
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<td>Qwen2.5-VL-3B-Instruct</td><td>27.0</td><td>28.6</td><td>37.6</td><td>31.9</td><td>33.1</td>
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</tr>
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<tr>
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<td>Qwen3-VL-2B-Instruct</td><td>50.3</td><td>28.9</td><td>34.5</td><td>36.9</td><td>35.6</td>
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</tr>
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<tr>
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<td>MindCube-3B-RawQA-SFT</td><td>17.2</td><td>1.7</td><td>51.7</td><td>24.1</td><td>6.3</td>
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</tr>
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<tr>
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<td>SpatialLadder-3B</td><td>44.8</td><td>27.4</td><td>43.4</td><td>39.8</td><td>27.9</td>
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</tr>
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<tr>
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<td>SpatialMLLM-4B</td><td>46.3</td><td>26.1</td><td>33.4</td><td>34.6</td><td>18.0</td>
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</tr>
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<tr>
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<td>VST-3B-SFT</td><td><strong>57.9</strong></td><td>30.2</td><td>35.9</td><td><strong>52.8</strong></td><td>35.8</td>
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</tr>
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<tr>
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<td>Cambrian-S-3B</td><td>57.3</td><td>25.2</td><td>32.5</td><td>39.0</td><td>28.3</td>
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</tr>
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<tr>
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<td><strong>SenseNova-SI-1.1-Qwen2.5-VL-3B</strong></td>
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<td>54.9</strong></td>
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<td><strong>30.8</strong></td>
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<td><strong>52.6</strong></td>
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<td>43.5</td>
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<td><strong>37.8</strong></td>
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</tr>
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<tr>
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<td colspan="6" align="center"><em>Proprietary Models</em></td>
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</tr>
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<tr>
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<td>Gemini-2.5-pro-2025-06</td><td>53.5</td><td>38.0</td><td>57.6</td><td>46.0</td><td>57.0</td>
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</tr>
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<tr>
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<td>Grok-4-2025-07-09</td><td>47.9</td><td>37.8</td><td>63.5</td><td>43.2</td><td>47.0</td>
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</tr>
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<tr>
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<td>GPT-5-2025-08-07</td><td>55.0</td><td>41.8</td><td>56.3</td><td>45.5</td><td>61.8</td>
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</tr>
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</tbody>
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</table>
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24
added_tokens.json
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
|
||||
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|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
7
chat_template.jinja
Normal file
7
chat_template.jinja
Normal file
@@ -0,0 +1,7 @@
|
||||
{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
|
||||
You are a helpful assistant.<|im_end|>
|
||||
{% endif %}<|im_start|>{{ message['role'] }}
|
||||
{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
|
||||
{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
|
||||
{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
|
||||
{% endif %}
|
||||
140
config.json
Normal file
140
config.json
Normal file
@@ -0,0 +1,140 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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"max_window_layers": 70,
|
||||
"model_type": "qwen2_5_vl",
|
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"num_attention_heads": 16,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 2,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
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|
||||
"rope_type": "default",
|
||||
"type": "default"
|
||||
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|
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|
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|
||||
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|
||||
"_name_or_path": "output/qwen2_5_3b_vsi/checkpoint-1500",
|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
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|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
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|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 128000,
|
||||
"max_window_layers": 70,
|
||||
"model_type": "qwen2_5_vl_text",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"sliding_window": null,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"in_chans": 3,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"temporal_patch_size": 2,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "image-text-to-text", "allow_remote": true}
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 151643,
|
||||
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|
||||
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|
||||
}
|
||||
BIN
merges.txt
(Stored with Git LFS)
Normal file
BIN
merges.txt
(Stored with Git LFS)
Normal file
Binary file not shown.
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
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||||
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model-00002-of-00002.safetensors
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3
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Normal file
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||||
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833
model.safetensors.index.json
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833
model.safetensors.index.json
Normal file
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31
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208
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||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
43
video_preprocessor_config.json
Normal file
43
video_preprocessor_config.json
Normal file
@@ -0,0 +1,43 @@
|
||||
{
|
||||
"crop_size": null,
|
||||
"data_format": "channels_first",
|
||||
"default_to_square": true,
|
||||
"device": null,
|
||||
"do_center_crop": null,
|
||||
"do_convert_rgb": true,
|
||||
"do_normalize": true,
|
||||
"do_rescale": true,
|
||||
"do_resize": true,
|
||||
"do_sample_frames": false,
|
||||
"fps": null,
|
||||
"image_mean": [
|
||||
0.48145466,
|
||||
0.4578275,
|
||||
0.40821073
|
||||
],
|
||||
"image_std": [
|
||||
0.26862954,
|
||||
0.26130258,
|
||||
0.27577711
|
||||
],
|
||||
"input_data_format": null,
|
||||
"max_frames": 768,
|
||||
"max_pixels": 147456,
|
||||
"merge_size": 2,
|
||||
"min_frames": 4,
|
||||
"min_pixels": 3136,
|
||||
"num_frames": null,
|
||||
"pad_size": null,
|
||||
"patch_size": 14,
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"resample": 3,
|
||||
"rescale_factor": 0.00392156862745098,
|
||||
"return_metadata": false,
|
||||
"size": {
|
||||
"longest_edge": 147456,
|
||||
"shortest_edge": 3136
|
||||
},
|
||||
"temporal_patch_size": 2,
|
||||
"video_metadata": null,
|
||||
"video_processor_type": "Qwen2VLVideoProcessor"
|
||||
}
|
||||
BIN
vocab.json
(Stored with Git LFS)
Normal file
BIN
vocab.json
(Stored with Git LFS)
Normal file
Binary file not shown.
Reference in New Issue
Block a user