Instructions to use nnpy/blip-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nnpy/blip-image-captioning 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="nnpy/blip-image-captioning")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("nnpy/blip-image-captioning") model = AutoModelForMultimodalLM.from_pretrained("nnpy/blip-image-captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from nnpy/blip-image-captioning: direct link, hf CLI and curl.
- Browser
- Download file 990 MB
-
https://huggingface.co/nnpy/blip-image-captioning/resolve/main/model.safetensors
- Command line
-
hf download hf://nnpy/blip-image-captioning/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/nnpy/blip-image-captioning/resolve/main/model.safetensors
990 MB
- Xet hash:
- e1ac5aeb6ebb8fe036f1cb290c03824f0f73e72dca37c4f7968fb16b482d38df
- Size of remote file:
- 990 MB
- SHA256:
- e84832b8a5f3ecad2b0ffede0e41ed609e4c0d75cdbb92d62be140cefb108814
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