Transformers
PyTorch
t5
text2text-generation
protein-generation
sequence-generation
conditional-generation
text-generation-inference
Instructions to use MoMA-LAAS/idr-prop2seq-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoMA-LAAS/idr-prop2seq-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MoMA-LAAS/idr-prop2seq-model") model = AutoModelForSeq2SeqLM.from_pretrained("MoMA-LAAS/idr-prop2seq-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MoMA-LAAS/idr-prop2seq-model: direct link, hf CLI and curl.
- Browser
- Download file 806 MB
-
https://huggingface.co/MoMA-LAAS/idr-prop2seq-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MoMA-LAAS/idr-prop2seq-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MoMA-LAAS/idr-prop2seq-model/resolve/main/pytorch_model.bin
806 MB
- Xet hash:
- ff0b25a200d7db0476d3efd2d1859585a823f4fd81060b4403406c01d33f128e
- Size of remote file:
- 806 MB
- SHA256:
- f12d0ce47b1fce70d279362b2cda983141b185e4c2069e8a59d2c259e7cb33ed
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