Image-to-Text
Transformers
Safetensors
English
vision-encoder-decoder
image-text-to-text
vit
bert
vision
caption
captioning
image
Instructions to use cnmoro/mini-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cnmoro/mini-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/mini-image-captioning")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("cnmoro/mini-image-captioning") model = AutoModelForMultimodalLM.from_pretrained("cnmoro/mini-image-captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22f895fe3aeda5401cd6e0a0694f4e4b6aac6f6c8d41970eafb42359b412681f
- Size of remote file:
- 137 MB
- SHA256:
- 5007211dd04f219b8a0fd24fbb7cfc91a8bb4894bfbe0ea231893a91d7f39553
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.