Image-to-Text
Transformers
Safetensors
vision-encoder-decoder
image-text-to-text
Generated from Trainer
Instructions to use mo-thecreator/ViT-GPT2-Image-Captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mo-thecreator/ViT-GPT2-Image-Captioning 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="mo-thecreator/ViT-GPT2-Image-Captioning")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("mo-thecreator/ViT-GPT2-Image-Captioning") model = AutoModelForImageTextToText.from_pretrained("mo-thecreator/ViT-GPT2-Image-Captioning") - Notebooks
- Google Colab
- Kaggle
Mohammed Abdeldayem commited on
Upload ViT-GPT2 model
Browse files- config.json +1 -1
- model.safetensors +1 -1
- preprocessor_config.json +1 -1
- training_args.bin +1 -1
config.json
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{
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"_name_or_path": "motheecreator/ViT-GPT2-
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"architectures": [
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"VisionEncoderDecoderModel"
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"_name_or_path": "motheecreator/ViT-GPT2-Image-Captioning",
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"architectures": [
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"VisionEncoderDecoderModel"
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model.safetensors
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preprocessor_config.json
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"image_std": [
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"image_processor_type": "ViTFeatureExtractor",
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"image_std": [
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training_args.bin
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