Image-to-Text
Transformers
Safetensors
vision-encoder-decoder
image-text-to-text
Generated from Trainer
image-captioning
Instructions to use mo-thecreator/ViT-GPT2-Image_Captioning_model 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_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="mo-thecreator/ViT-GPT2-Image_Captioning_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("mo-thecreator/ViT-GPT2-Image_Captioning_model") model = AutoModelForMultimodalLM.from_pretrained("mo-thecreator/ViT-GPT2-Image_Captioning_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df0e225149424c7e837ffd4de83ea3ce8afd55d7bf73bee4e1957492409981fe
- Size of remote file:
- 5.3 kB
- SHA256:
- 0bee922a139f7bb463929ce1596c39f316085f09c6abce09f9339d0ec028e22d
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