Instructions to use jeff-RQ/new-test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeff-RQ/new-test-model 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="jeff-RQ/new-test-model")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("jeff-RQ/new-test-model") model = AutoModelForVisualQuestionAnswering.from_pretrained("jeff-RQ/new-test-model") - Notebooks
- Google Colab
- Kaggle
Update handler.py
Browse files- handler.py +1 -1
handler.py
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@@ -15,7 +15,7 @@ class EndpointHandler:
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def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
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# process input
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-
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text = data.pop("text", data)
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image_string = base64.b64decode(data["image"])
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def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
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# process input
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data = data.pop("inputs", data)
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text = data.pop("text", data)
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image_string = base64.b64decode(data["image"])
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