Image-to-Text
Transformers
PyTorch
Russian
vision-encoder-decoder
image-text-to-text
image-captioning
Instructions to use tuman/vit-rugpt2-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tuman/vit-rugpt2-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="tuman/vit-rugpt2-image-captioning")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("tuman/vit-rugpt2-image-captioning") model = AutoModelForMultimodalLM.from_pretrained("tuman/vit-rugpt2-image-captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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