Image-to-Text
Transformers
PyTorch
Chinese
vision-encoder-decoder
image-text-to-text
image-captioning
Instructions to use Maciel/Muge-Image-Caption with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maciel/Muge-Image-Caption 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="Maciel/Muge-Image-Caption")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Maciel/Muge-Image-Caption") model = AutoModelForMultimodalLM.from_pretrained("Maciel/Muge-Image-Caption", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Maciel/Muge-Image-Caption: direct link, hf CLI and curl.
- Browser
- Download file 1.76 MB
-
https://huggingface.co/Maciel/Muge-Image-Caption/resolve/main/config.json
- Command line
-
hf download hf://Maciel/Muge-Image-Caption/config.json
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curl -L -o config.json https://huggingface.co/Maciel/Muge-Image-Caption/resolve/main/config.json
1.76 MB
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