Instructions to use abhyudit309/openvla-7b-finetuned-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abhyudit309/openvla-7b-finetuned-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="abhyudit309/openvla-7b-finetuned-v1", trust_remote_code=True)# Load model directly from transformers import AutoModelForVision2Seq model = AutoModelForVision2Seq.from_pretrained("abhyudit309/openvla-7b-finetuned-v1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db05ae4d07d3a6072af101b830f009e3ead5fe9c64734d21724e58c488be3ffd
- Size of remote file:
- 4.95 GB
- SHA256:
- 35cde23ef437b145f648f17f493fab74fec9751e01aa5c5af9ee7dda34f7db11
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.