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, 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 ·
cc9fd90
1
Parent(s): 44f04d5
Add TF weights
Browse filesModel 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!
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:9fb4b74fce7b1f965cbc7bc9b58db27cfaaebfa0d86e27d49fec877288a11852
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size 1879709248
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