Instructions to use hf-tiny-model-private/tiny-random-Blip2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Blip2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-Blip2Model")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Blip2Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Blip2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27f5d2c9497fc97b38170042d38c6425b11f410d77e9305ebe6b898ecfd34791
- Size of remote file:
- 965 kB
- SHA256:
- 62c08448903482892de2e3693970d70ca5800141625da0495ed8b15aecb44e9a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.