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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 879172818e07711072be1734ed76702ac466458000510c64a9b0c6a7e1c9cae7
- Size of remote file:
- 899 kB
- SHA256:
- 739d86c91e81049d6340f4a1aef11eb8648af0175bb45b9e5807a239d1ebead7
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