Instructions to use tattabio/gLM2_650M_bgc_decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tattabio/gLM2_650M_bgc_decoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tattabio/gLM2_650M_bgc_decoder", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("tattabio/gLM2_650M_bgc_decoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from tattabio/gLM2_650M_bgc_decoder: direct link, hf CLI and curl.
- Browser
- Download file 1.71 kB
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https://huggingface.co/tattabio/gLM2_650M_bgc_decoder/resolve/main/README.md
- Command line
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hf download hf://tattabio/gLM2_650M_bgc_decoder/README.md
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curl -L -o README.md https://huggingface.co/tattabio/gLM2_650M_bgc_decoder/resolve/main/README.md
1.71 kB
| license: cc-by-nc-4.0 | |
| base_model: tattabio/gLM2_650M | |
| library_name: transformers | |
| pipeline_tag: fill-mask | |
| # gLM2 650M BGC Decoder | |
| A 650M-parameter masked discrete diffusion model for conditional generation and redesign of biosynthetic gene clusters. The model supports antiSMASH domain conditioning and sequences up to 16,384 tokens. | |
| ```python | |
| import torch | |
| from transformers import AutoModelForMaskedLM, AutoTokenizer | |
| model_id = "tattabio/gLM2_650M_bgc_decoder" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) | |
| model = AutoModelForMaskedLM.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.bfloat16, | |
| trust_remote_code=True, | |
| ).cuda() | |
| ``` | |
| Inference code: https://github.com/TattaBio/gLM2_decoder | |
| ## Citation | |
| ``` | |
| @article {Lanclos2026.09.11.750945, | |
| author = {Lanclos, Nathan and Ibrahim, Kyrellos and Cornman, Andre and Huang, Marco and Gill, Vikram and Jain, Aalini and Abraham, Jonathan and Gin, Jennifer and Chen, Yan and Petzold, Christopher and Baerwald, Justin and Kortemme, Tanja and Keasling, Jay and Hwang, Yunha}, | |
| title = {Generative Design of New-to-nature Biosynthetic Assembly Lines with Genomic Language Modeling}, | |
| elocation-id = {2026.09.11.750945}, | |
| year = {2026}, | |
| doi = {10.64898/2026.09.11.750945}, | |
| publisher = {Cold Spring Harbor Laboratory}, | |
| URL = {https://www.biorxiv.org/content/early/2026/09/21/2026.09.11.750945}, | |
| eprint = {https://www.biorxiv.org/content/early/2026/09/21/2026.09.11.750945.full.pdf}, | |
| journal = {bioRxiv} | |
| } | |
| ``` | |
| ## License | |
| Model weights are released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. The weights are freely available for academic and research purposes. |