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---
license: mit
datasets:
- ZINC-22
language:
- en
tags:
- molecular-generation
- drug-discovery
- llama
- flash-attention
pipeline_tag: text-generation
---
# NovoMolGen
NovoMolGen is a family of molecular foundation models trained on 1.5 billion ZINC‑22 molecules using Llama architectures and FlashAttention. It achieves state‑of‑the‑art performance on both unconstrained and goal‑directed molecule generation tasks.
## How to load
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("chandar-lab/NovoMolGen_300M_SMILES_BPE", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("chandar-lab/NovoMolGen_300M_SMILES_BPE", trust_remote_code=True)
```
## Quickstart
```python
outputs = model.sample(tokenizer=tokenizer, batch_size=4)
print(outputs['SMILES'])
```
## Citation
```bibtex
@article{chitsaz2024novomolgen,
title={NovoMolGen: Rethinking Molecular Language Model Pretraining},
author={Chitsaz, Kamran and Balaji, Roshan and Fournier, Quentin and Bhatt, Nirav Pravinbhai and Chandar, Sarath},
journal={arXiv preprint},
year={2025},
}
``` |