Instructions to use Salesforce/blip-image-captioning-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/blip-image-captioning-base 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-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = AutoModelForMultimodalLM.from_pretrained("Salesforce/blip-image-captioning-base", 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=5.970e-03; Maximum crossload hidden layer difference=1.053e-01; Maximum conversion output difference=5.970e-03; Maximum conversion hidden layer difference=1.053e-01; CAUTION: The maximum admissible error was manually increased to 0.2!588a7b6
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Update README.md (#11)
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Younes Belkada08a482e