Instructions to use hf-internal-testing/tiny-random-MBartModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MBartModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-MBartModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MBartModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-MBartModel") - 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 sha256:5a7781be98498ce94ec5a57ca6a4b6a8199c5cce0e751b6a4567e10d22ea27d4
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onnx/decoder_model_merged.onnx
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oid sha256:4535ab25e5e5d1c512dda03fbc18ada42424acd284424c0f4525ab655ccaba87
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size 16224720
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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:e68bd58d730fea7928aa121c905a3cdebc897141081e8e2713284ad1521625cb
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size 16110688
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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:ad45fb8a2c03ef25025c3f7358c8c61c982af3fbf06514c6bf13f62d554cfc49
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size 16068525
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