Instructions to use hf-internal-testing/tiny-random-BlenderbotModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BlenderbotModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-BlenderbotModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BlenderbotModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-BlenderbotModel") - 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:e78557b926b6827a8f7e40978f9cb4ea81f0e863e9fe8235c8d5db7d9b4ce21b
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size 192746
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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 sha256:48514452fb556b32989911117edfae6f42534ca7a9215ad528123b1531b10a05
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size 278998
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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:e7aa6c4d36d82b33254a4ef51b62e4b6d51c0d9a2aadb3f9c1359e94b3d76333
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size 169466
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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 sha256:0429b5beb33220f59be70064bd5268abfa8ccc52a89e325fbcba7587dd8f0e8a
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size 128138
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