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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("mo-thecreator/ViT-GPT2-Image-Captioning") model = AutoModelForMultimodalLM.from_pretrained("mo-thecreator/ViT-GPT2-Image-Captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91a1ade079494d0d3945cb757a25b4ae80299eea73415a6c6a05fa52deebdfa4
- Size of remote file:
- 5.3 kB
- SHA256:
- cc778c29dd169daae9ef298ffd614bd10d12de1ecd4350610ac3083c01cbcd9a
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