Instructions to use mimi/test_KE-T5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mimi/test_KE-T5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mimi/test_KE-T5") model = AutoModelForSeq2SeqLM.from_pretrained("mimi/test_KE-T5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
add model
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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{
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"_name_or_path": "
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"architectures": [
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"T5ForConditionalGeneration"
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],
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{
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"_name_or_path": "mimi/test_KE-T5",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 990051984
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version https://git-lfs.github.com/spec/v1
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oid sha256:d183e5e03be2657c18c73657c0347a4f78a1b44e8ee89924751ad64dc0c57c6f
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size 990051984
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