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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd437db94f5286bc88fbf96eea06d65cd6a0228f076ec3ef87716a3a9532fbbe
- Size of remote file:
- 4.73 kB
- SHA256:
- c0a2ac50f309f8c9847a82159d9a9ac78e7a2325898793a1789b7a803a96a996
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