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)# 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
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.
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
TODO
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.
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tattabio/gLM2_650M