Create README.md

README.md

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+---
+library_name: transformers
+tags:
+- robotics
+- vla
+- image-text-to-text
+- multimodal
+- pretraining
+license: mit
+language:
+- en
+pipeline_tag: image-text-to-text
+---
+
+# MiniVLA 1B VQ Trained on Bridge V2 (Prismatic-Compatible Version)
+
+<b>This checkpoint is in a format that is compatible with the training script from the original [Prismatic VLMs project codebase](https://github.com/TRI-ML/prismatic-vlms), which the OpenVLA
+team built on top of to develop the OpenVLA model.</b>
+
+This Prismatic-compatible checkpoint may be useful if you wish to <b>fully fine-tune</b> MiniVLA (all 1 billion parameters) via native PyTorch Fully
+Sharded Data Parallel (FSDP) using the Prismatic VLMs training script. If you instead wish to do Parameter-Efficient Fine-Tuning via LoRA, you
+can use the MiniVLA checkpoint linked above, which is compatible with the Hugging Face `transformers` library. We recommend fine-tuning via LoRA if
+you do not have sufficient compute to fully fine-tune a 1B-parameter model (e.g., multiple A100/H100 GPUs).
+
+## Usage Instructions
+
+See the [MiniVLA GitHub README](https://github.com/Stanford-ILIAD/openvla-mini/blob/main/README.md) for instructions on how to use this checkpoint for full fine-tuning.
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@article{belkhale24minivla,
+    title={MiniVLA: A Better VLA with a Smaller Footprint},
+    author={Suneel Belkhale and Dorsa Sadigh},
+    url={https://github.com/Stanford-ILIAD/openvla-mini}
+    year={2024}
+} 
+```