Instructions to use optimum/t5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum/t5-small with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" 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("translation", model="optimum/t5-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("optimum/t5-small") model = AutoModelForSeq2SeqLM.from_pretrained("optimum/t5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f98f9202a9b7131f622621e9cd1f5a3626f86601fb3ec34afca2cfd5bc02c38
- Size of remote file:
- 232 MB
- SHA256:
- 0a1451011d61bcc796a87b7306c503562e910f110f884d0cc08532972c2cc584
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