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
Upload onnx model
Browse files- decoder.onnx +3 -0
- decoder_with_past.onnx +3 -0
- encoder.onnx +3 -0
decoder.onnx
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oid sha256:64f91adec5263c5a8db88a931da1478433562ba86208512383c5425749410f2a
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decoder_with_past.onnx
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oid sha256:ebbed107865d277fab00cd56447ee0ede254b6bf6b755c23dd8a7a874e7de6fc
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size 219785197
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encoder.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:88eece229f09ab54b985af7703ab7129562a4a3da7c28e4c5593d182b8ed7bac
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size 141359413
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