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bing-yan
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consistency

PyTorch
chemistry
gpt2
representation-consistency
Model card Files Community

Consistency

Architecture: GPT-2 small
Task: Forward reaction prediction (SMILES or IUPAC representation for the input)
Training data: 80k mapped reactions
Checkpoint size: 124M params

Usage 

from transformers import AutoModelForCausalLM, AutoTokenizer
# model trained on SMILES input, without KL divergence loss
tok = AutoTokenizer.from_pretrained("bing-yan/consistency", subfolder="nokl-smiles")
model = AutoModelForCausalLM.from_pretrained("bing-yan/consistency", subfolder="nokl-smiles")

# model trained on IUPAC input, without KL divergence loss
tok = AutoTokenizer.from_pretrained("bing-yan/consistency", subfolder="nokl-iupac")
model = AutoModelForCausalLM.from_pretrained("bing-yan/consistency", subfolder="nokl-iupac")

# model trained on SMILES input, with KL divergence loss
tok = AutoTokenizer.from_pretrained("bing-yan/consistency", subfolder="kl-smiles")
model = AutoModelForCausalLM.from_pretrained("bing-yan/consistency", subfolder="nokl-smiles")

# model trained on IUPAC input, with KL divergence loss
tok = AutoTokenizer.from_pretrained("bing-yan/consistency", subfolder="kl-iupac")
model = AutoModelForCausalLM.from_pretrained("bing-yan/consistency", subfolder="nokl-iupac")

## Citations

- GitHub: [github.com/bingyan4science/consistency](https://github.com/bingyan4science/consistency)
- Dataset (Zenodo): [https://doi.org/10.5281/zenodo.14430369](https://doi.org/10.5281/zenodo.14430369)
- Paper: [Inconsistency of LLMs in Molecular Representations](https://chemrxiv.org/engage/chemrxiv/article-details/675b9de27be152b1d0ced2b5)
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