Image-to-Text
Transformers
Safetensors
English
vision-encoder-decoder
image-text-to-text
vit
bert
vision
caption
captioning
image
Instructions to use cnmoro/tiny-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cnmoro/tiny-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="cnmoro/tiny-image-captioning")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("cnmoro/tiny-image-captioning") model = AutoModelForMultimodalLM.from_pretrained("cnmoro/tiny-image-captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from cnmoro/tiny-image-captioning: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/cnmoro/tiny-image-captioning/resolve/main/tokenizer.json
- Command line
-
hf download hf://cnmoro/tiny-image-captioning/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/cnmoro/tiny-image-captioning/resolve/main/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.