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")# 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
- Xet hash:
- 44885fe0f86b2af934037e56afe0c4629c0961fca53f6160a64715f40e1f8895
- Size of remote file:
- 1.86 GB
- SHA256:
- b0cc232d34dcd7b4eeb40aded4323f6d02046d84714e703e623cfd6e22156491
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