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echarlaix
/
t5-small-int8-dynamic

Translation
Transformers
ONNX
t5
text-generation
int8
summarization
Model card Files Files and versions
xet
Community
1

Instructions to use echarlaix/t5-small-int8-dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use echarlaix/t5-small-int8-dynamic 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="echarlaix/t5-small-int8-dynamic")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelWithLMHead
    
    tokenizer = AutoTokenizer.from_pretrained("echarlaix/t5-small-int8-dynamic")
    model = AutoModelWithLMHead.from_pretrained("echarlaix/t5-small-int8-dynamic")
  • Notebooks
  • Google Colab
  • Kaggle
t5-small-int8-dynamic
150 MB
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  • 2 contributors
History: 3 commits
echarlaix's picture
echarlaix HF Staff
lbourdois's picture
lbourdois
Add multilingual to the language tag (#1)
f09be19 over 2 years ago
  • .gitattributes
    1.34 kB
    initial commit almost 4 years ago
  • README.md
    1.36 kB
    Add multilingual to the language tag (#1) over 2 years ago
  • config.json
    1.39 kB
    Upload model almost 4 years ago
  • decoder_model.onnx
    58.3 MB
    xet
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  • decoder_with_past_model.onnx
    55.1 MB
    xet
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  • encoder_model.onnx
    35.4 MB
    xet
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  • ort_config.json
    736 Bytes
    Upload model almost 4 years ago
  • tokenizer.json
    1.39 MB
    Upload model almost 4 years ago