library_name: transformers
tags:
- robotics
- vla
- image-text-to-text
- multimodal
- pretraining
license: mit
language:
- en
pipeline_tag: image-text-to-text
MiniVLA Image History (T=2) VQ 1B (Prismatic-Compatible Version)
This checkpoint is in a format that is compatible with the training script from the original Prismatic VLMs project codebase, which the OpenVLA team built on top of to develop the OpenVLA model.
This Prismatic-compatible checkpoint may be useful if you wish to fully fine-tune 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 for instructions on how to use this checkpoint for full fine-tuning.
Citation
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}
}