Instructions to use hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration 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-Blip2ForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration") model = AutoModelForMultimodalLM.from_pretrained("hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 704ec56d99d9c8f6b47e4b82f65017560affb63427bcf125b1a8dfbb18a7d423
- Size of remote file:
- 965 kB
- SHA256:
- eed1b8febdf396078cde3546e7a892073d0cb4756164f4e7128fc860e099acac
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