Instructions to use noamrot/FuseCap_Image_Captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noamrot/FuseCap_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="noamrot/FuseCap_Image_Captioning")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("noamrot/FuseCap_Image_Captioning") model = AutoModelForMultimodalLM.from_pretrained("noamrot/FuseCap_Image_Captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from noamrot/FuseCap_Image_Captioning: direct link, hf CLI and curl.
- Browser
- Download file 896 MB
-
https://huggingface.co/noamrot/FuseCap_Image_Captioning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://noamrot/FuseCap_Image_Captioning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/noamrot/FuseCap_Image_Captioning/resolve/main/pytorch_model.bin
896 MB
- Xet hash:
- 37f8720ea74d264c7684784853283d17905060b895682779f505ac414463a258
- Size of remote file:
- 896 MB
- SHA256:
- f76f83fe6e67313f85b90fab7504558eba18f8c695412085e7fa30454c971ba1
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