Instructions to use quadranttechnologies/qhub-blip-image-captioning-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quadranttechnologies/qhub-blip-image-captioning-finetuned with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="quadranttechnologies/qhub-blip-image-captioning-finetuned")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("quadranttechnologies/qhub-blip-image-captioning-finetuned") model = AutoModelForMultimodalLM.from_pretrained("quadranttechnologies/qhub-blip-image-captioning-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9263cc29dade779501bbebba16dba33b41821a515f6ba45613da88f25e0dac24
- Size of remote file:
- 5.24 kB
- SHA256:
- 4a611262c67fe18838d640d65316bac01d2afc9abae5499b382c35c06b75d0b0
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