Instructions to use nanom/vizwiz-t5-base-ft-multicap-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nanom/vizwiz-t5-base-ft-multicap-v1 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" 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("question-answering", model="nanom/vizwiz-t5-base-ft-multicap-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("nanom/vizwiz-t5-base-ft-multicap-v1") model = AutoModelForSeq2SeqLM.from_pretrained("nanom/vizwiz-t5-base-ft-multicap-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nanom/vizwiz-t5-base-ft-multicap-v1: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/nanom/vizwiz-t5-base-ft-multicap-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nanom/vizwiz-t5-base-ft-multicap-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nanom/vizwiz-t5-base-ft-multicap-v1/resolve/main/pytorch_model.bin
892 MB
- Xet hash:
- 1ac5e9f94cac70c6cf307003c4f33022ed8410fa013a70b815c2e2630a296980
- Size of remote file:
- 892 MB
- SHA256:
- 9f3e98f9a797aa28f9dd1261986adec794f333c57e71815a2ec6bda03ef64025
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