Instructions to use Salesforce/blip-image-captioning-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/blip-image-captioning-large 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="Salesforce/blip-image-captioning-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = AutoModelForMultimodalLM.from_pretrained("Salesforce/blip-image-captioning-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add TF weights
Model converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its PyTorch counterpart. Maximum crossload output difference=1.395e-03; Maximum crossload hidden layer difference=1.080e+00; Maximum conversion output difference=1.395e-03; Maximum conversion hidden layer difference=1.080e+00; CAUTION: The maximum admissible error was manually increased to 2.0!cc9fd90
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- Update README.md (18221a40e4da5b2ff3f880de931b7ac631e39935) Co-authored-by: Daniele Acquaviva <dacquaviva@users.noreply.huggingface.co>18903c0
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initial commit
Younes Belkadaeb32014