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")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = AutoModelForImageTextToText.from_pretrained("Salesforce/blip-image-captioning-large") - Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
#1
by ybelkada - opened
- tokenizer_config.json +4 -0
tokenizer_config.json
CHANGED
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@@ -7,6 +7,10 @@
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"name_or_path": "ybelkada/blip-image-captioning-base",
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"never_split": null,
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"pad_token": "[PAD]",
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"processor_class": "BlipProcessor",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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"name_or_path": "ybelkada/blip-image-captioning-base",
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"never_split": null,
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"pad_token": "[PAD]",
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"processor_class": "BlipProcessor",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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