DPLM (diffusion protein language model) is a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences. Specifically, DPLM exhibits impressive performance in protein sequence generation, motif scaffolding, inverse folding, and representation learning. For more detailed information about DPLM, please refer to our paper Diffusion Language Models Are Versatile Protein Learners.
This repository contains the DPLM model checkpoint of 3B parameters. Please refer to our github repository for code and usage. For example, you can load DPLM model as below:
from byprot.models.lm.dplm import DiffusionProteinLanguageModel
model_name = "airkingbd/dplm_3B"
dplm = DiffusionProteinLanguageModel.from_pretrained(model_name)
All DPLM checkpoints are available in the table below:
News: welcome to check our new work DPLM-2: A Multimodal Diffusion Protein Language Model, a multimodal protein foundation model that extends DPLM to simultaneously model, understand, and generate both sequences and structures!
DPLM (diffusion protein language model) is a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences. Specifically, DPLM exhibits impressive performance in protein sequence generation, motif scaffolding, inverse folding, and representation learning. For more detailed information about DPLM, please refer to our paper Diffusion Language Models Are Versatile Protein Learners.
This repository contains the DPLM model checkpoint of 3B parameters. Please refer to our github repository for code and usage. For example, you can load DPLM model as below:
from byprot.models.lm.dplm import DiffusionProteinLanguageModel
model_name = "airkingbd/dplm_3B"
dplm = DiffusionProteinLanguageModel.from_pretrained(model_name)
All DPLM checkpoints are available in the table below:
News: welcome to check our new work DPLM-2: A Multimodal Diffusion Protein Language Model, a multimodal protein foundation model that extends DPLM to simultaneously model, understand, and generate both sequences and structures!