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README.md

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----
-license: apache-2.0
----
-
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/iVKgqK6vTzCpCLVnWxmjA.png)
-
-# 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},
-}
-```
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