Instructions to use devlocalhost/blip2-opt-2.7b-xray-captions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devlocalhost/blip2-opt-2.7b-xray-captions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="devlocalhost/blip2-opt-2.7b-xray-captions")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("devlocalhost/blip2-opt-2.7b-xray-captions") model = AutoModelForMultimodalLM.from_pretrained("devlocalhost/blip2-opt-2.7b-xray-captions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 761c5d66f3b8b1f7d597bee96237c72bbc0c68c90c83aca25375974c0d9b5de4
- Size of remote file:
- 3.89 GB
- SHA256:
- dcfe9d78c9ef97b7f627a0961aab40eb058856083e659b3e043b32e44a145527
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