Image-to-Text
Transformers
PyTorch
ONNX
vision-encoder-decoder
image-text-to-text
image-captioning
Eval Results (legacy)
Instructions to use tarekziade/test-push with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tarekziade/test-push 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="tarekziade/test-push")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("tarekziade/test-push") model = AutoModelForMultimodalLM.from_pretrained("tarekziade/test-push") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69e6aace4f5b37d7c148af0446fb61cab6e5db78f07dfe3d59ce762642a36a70
- Size of remote file:
- 730 MB
- SHA256:
- 1b26ea0227ceb2870c6d45cf7000830cea7d5a0727d6996941d1564195d5a2e6
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