Instructions to use luqh/ClinicalT5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luqh/ClinicalT5-large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("luqh/ClinicalT5-large") model = AutoModelForSeq2SeqLM.from_pretrained("luqh/ClinicalT5-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from luqh/ClinicalT5-large: direct link, hf CLI and curl.
- Browser
- Download file 2.95 GB
-
https://huggingface.co/luqh/ClinicalT5-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://luqh/ClinicalT5-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/luqh/ClinicalT5-large/resolve/main/flax_model.msgpack
2.95 GB
- Xet hash:
- e7daae2cdbc0652714c80a59cfb8ad18f559720833b5821366ad2508b8c59790
- Size of remote file:
- 2.95 GB
- SHA256:
- 1332aa98e04ca6abc81dc69929d16b5a601b1a0e634e744a0826403df83c4b02
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