Instructions to use akshat3492/mT5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akshat3492/mT5 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="akshat3492/mT5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("akshat3492/mT5") model = AutoModelForSeq2SeqLM.from_pretrained("akshat3492/mT5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from akshat3492/mT5: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/akshat3492/mT5/resolve/main/tokenizer.json
- Command line
-
hf download hf://akshat3492/mT5/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/akshat3492/mT5/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 13703d1accb02521d6d7719064d5c0ad97aa67ea640be335621e3658f16b553d
- Size of remote file:
- 16.3 MB
- SHA256:
- da4980af4e0649bb07a8cffdad7344bba0401a39dc67fb0256b4da603aae65b9
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