Instructions to use hf-internal-testing/tiny-random-t5-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-t5-v1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-t5-v1.1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5-v1.1") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-t5-v1.1") - Notebooks
- Google Colab
- Kaggle
[Awaiting approval] Upload ONNX weights
#2
by Xenova HF Staff - opened
onnx/decoder_model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:350cc2faf27b40c6bd072255803bdd4bc3e8ea2f48408ad6798ce12830ccb285
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size 497113
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onnx/decoder_model_merged.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:f7876384ced84766873ba89625a5b84d06a7aba06187ef3821656429b54d6308
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size 637834
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onnx/decoder_with_past_model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:a01fc260459adfde401fb2c0555e91f79d9514381d6182c3ae92f041631361df
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size 448157
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onnx/encoder_model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:7b37340f4c5d8ba61a5a7db05c94dc97ecce67c172d9ceab076d781861bbb014
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size 340342
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