Image-to-Text
Transformers
PyTorch
TensorBoard
vision-encoder-decoder
image-text-to-text
Generated from Trainer
Instructions to use larabe/testt1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use larabe/testt1 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="larabe/testt1")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("larabe/testt1") model = AutoModelForImageTextToText.from_pretrained("larabe/testt1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20acacc89d5f75be912ba78934e167a32770370187594e22d82bcd67f71186a7
- Size of remote file:
- 809 MB
- SHA256:
- 4e1c556c721a51676c2b46db7a9b42666b1d63443f7e5a80c78049f6fbca00a0
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