Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- wentao-yuan/robopoint-data
+base_model:
+- meta-llama/Llama-2-13b-chat-hf
+---
+# RoboPoint-v1-Llama2-13B-LoRA
+RoboPoint is an open-source vision-language model instruction-tuned on a mix of robotics and VQA data. Given an image with language instructions, it outputs precise action guidance as points.
+
+## Primary Use Cases
+RoboPoint can predict spatial affordances—where actions should be taken in relation to other entities—based on instructions. For example, it can identify free space on a shelf in front of the rightmost object.
+
+## Model Details
+This model was fine-tuned using [LoRA](https://arxiv.org/abs/2106.09685) from [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) and has 13 billion parameters.
+
+## Date
+This model was trained in June 2024.
+
+## Resources for More Information
+
+- Paper: https://arxiv.org/pdf/2406.10721
+- Code: https://github.com/wentaoyuan/RoboPoint
+- Website: https://robo-point.github.io
+
+## Training dataset
+See [wentao-yuan/robopoint-data](https://huggingface.co/datasets/wentao-yuan/robopoint-data).
+
+## Citation
+If you find our work helpful, please consider citing our paper.
+```
+@article{yuan2024robopoint,
+  title={RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics},
+  author={Yuan, Wentao and Duan, Jiafei and Blukis, Valts and Pumacay, Wilbert and Krishna, Ranjay and Murali, Adithyavairavan and Mousavian, Arsalan and Fox, Dieter},
+  journal={arXiv preprint arXiv:2406.10721},
+  year={2024}
+}
+```