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README.md

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+![image/png](https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/iVKgqK6vTzCpCLVnWxmjA.png)
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+# Model Card for SpaceLLaVA
+
+**SpaceLLaVA** uses LoRA to fine-tune [LLaVA](https://github.com/haotian-liu/LLaVA/tree/main) on a dataset designed with [VQASynth](https://github.com/remyxai/VQASynth/tree/main) to enhance spatial reasoning as in [SpatialVLM](https://spatial-vlm.github.io/) 
+
+## Model Details
+
+### Model Description
+
+This model uses data synthesis techniques and publically available models to reproduce the work described in SpatialVLM to enhance the spatial reasoning of multimodal models. 
+With a pipeline of expert models, we can infer spatial relationships between objects in a scene to create VQA dataset for spatial reasoning.
+
+
+- **Developed by:** remyx.ai
+- **Model type:** MultiModal Model, Vision Language Model, LLaVA
+- **License:** Apache-2.0
+- **Finetuned from model:** LLaVA
+
+### Model Sources
+
+- **Repository:** [VQASynth](https://github.com/remyxai/VQASynth/tree/main)
+- **Paper:** [SpatialVLM](https://arxiv.org/abs/2401.12168)
+
+## Uses
+
+Use this model to query spatial relationships between objects in a scene.
+
+## Citation
+
+@article{chen2024spatialvlm,
+  title = {SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities},
+  author = {Chen, Boyuan and Xu, Zhuo and Kirmani, Sean and Ichter, Brian and Driess, Danny and Florence, Pete and Sadigh, Dorsa and Guibas, Leonidas and Xia, Fei},
+  journal = {arXiv preprint arXiv:2401.12168},
+  year = {2024},
+  url = {https://arxiv.org/abs/2401.12168},
+}
+
+@misc{liu2023llava,
+      title={Visual Instruction Tuning}, 
+      author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
+      publisher={NeurIPS},
+      year={2023},
+}