Instructions to use zjunlp/MolGen-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zjunlp/MolGen-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-large") model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen-large") - Notebooks
- Google Colab
- Kaggle
Yin Fang commited on
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README.md
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@@ -10,7 +10,7 @@ MolGen was introduced in the paper ["Molecular Language Model as Multi-task Gene
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MolGen is the first pre-trained model that only produces chemically valid molecules.
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With a training corpus of over 100 million molecules in SELFIES representation, MolGen learns the intrinsic structural patterns of molecules by mapping corrupted SELFIES to their original forms.
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Specifically, MolGen employs a bidirectional Transformer as its encoder and an autoregressive Transformer as its decoder.
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Through its carefully designed multi-task prefix tuning (MPT), MolGen can generate molecules with desired properties, making it a valuable tool for molecular optimization.
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### BibTeX entry and citation info
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MolGen is the first pre-trained model that only produces chemically valid molecules.
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With a training corpus of over 100 million molecules in SELFIES representation, MolGen learns the intrinsic structural patterns of molecules by mapping corrupted SELFIES to their original forms.
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Specifically, MolGen employs a bidirectional Transformer as its encoder and an autoregressive Transformer as its decoder.
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Through its carefully designed multi-task molecular prefix tuning (MPT), MolGen can generate molecules with desired properties, making it a valuable tool for molecular optimization.
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### BibTeX entry and citation info
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