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README.md
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- multimodal
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- spatial
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- sptial understanding
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library_name: transformers
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---
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# Spatial-SSRL-7B
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📖<a href="https://arxiv.org/abs/2510.27606">Paper</a>| 🏠<a href="https://github.com/InternLM/Spatial-SSRL">Github</a> |🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-7B">Spatial-SSRL-7B Model</a> |
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🤗<a href="https://huggingface.co/datasets/internlm/Spatial-SSRL-81k">Spatial-SSRL-81k Dataset</a>
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Spatial-SSRL-7B is a large vision-language model targeting spatial understanding, built on the base of Qwen2.5-VL-7B. It's optimized by applying Spatial-SSRL, a lightweight self-supervised reinforcement learning
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paradigm which can scale RLVR efficiently. The model demonstrates strong spatial intelligence while preserving the original general visual capabilities of the base model.
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## 📢 News
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- 🚀 [2025/11/03] We have released the [🤗Spatial-SSRL-7B Model](https://huggingface.co/internlm/Spatial-SSRL-7B),and [🤗Spatial-SSRL-81k Dataset](https://huggingface.co/datasets/internlm/Spatial-SSRL-81k).
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- 🚀 [2025/11/02] We have released the [🏠Spatial-SSRL Repository](https://github.com/InternLM/Spatial-SSRL).
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</p>
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## 🛠️ Usage
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Here we provide a code snippet for you to start a simple trial of <strong>Spatial-SSRL-7B</strong> on your own device. You can download the model from 🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-7B">Spatial-SSRL-7B Model</a > before your trial!
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</p>
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- multimodal
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- spatial
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- sptial understanding
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- self-supervised learning
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library_name: transformers
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---
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# Spatial-SSRL-7B
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📖<a href="https://arxiv.org/abs/2510.27606">Paper</a>| 🏠<a href="https://github.com/InternLM/Spatial-SSRL">Github</a> |🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-7B">Spatial-SSRL-7B Model</a> |
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🤗<a href="https://huggingface.co/datasets/internlm/Spatial-SSRL-81k">Spatial-SSRL-81k Dataset</a> | 📰<a href="https://huggingface.co/papers/2510.27606">Daily Paper</a>
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Spatial-SSRL-7B is a large vision-language model targeting spatial understanding, built on the base of Qwen2.5-VL-7B. It's optimized by applying Spatial-SSRL, a lightweight self-supervised reinforcement learning
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paradigm which can scale RLVR efficiently. The model demonstrates strong spatial intelligence while preserving the original general visual capabilities of the base model.
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## 📢 News
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- 🚀 [2025/11/03] Now you can try out Spatial-SSRL-7B on [🤗Spatial-SSRL Space](https://huggingface.co/spaces/yuhangzang/Spatial-SSRL).
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- 🚀 [2025/11/03] We have released the [🤗Spatial-SSRL-7B Model](https://huggingface.co/internlm/Spatial-SSRL-7B),and [🤗Spatial-SSRL-81k Dataset](https://huggingface.co/datasets/internlm/Spatial-SSRL-81k).
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- 🚀 [2025/11/02] We have released the [🏠Spatial-SSRL Repository](https://github.com/InternLM/Spatial-SSRL).
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</p>
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## 🛠️ Usage
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To directly experience <strong>Spatial-SSRL-7B</strong>, you can try it out on [🤗Spatial-SSRL Space](https://huggingface.co/spaces/yuhangzang/Spatial-SSRL)!
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Here we provide a code snippet for you to start a simple trial of <strong>Spatial-SSRL-7B</strong> on your own device. You can download the model from 🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-7B">Spatial-SSRL-7B Model</a > before your trial!
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</p>
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