| --- |
| license: apache-2.0 |
| pipeline_tag: feature-extraction |
| library_name: transformers |
| --- |
| |
| # DPLM |
| 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](https://arxiv.org/abs/2402.18567). |
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| Project Page: https://bytedance.github.io/dplm/dplm-2.1 |
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| This repository contains the DPLM model checkpoint of 150M parameters. |
| Please refer to our [github repository](https://github.com/bytedance/dplm/tree/main) for code and usage. |
| For example, you can load DPLM model as below: |
| ``` |
| from byprot.models.lm.dplm import DiffusionProteinLanguageModel |
| model_name = "airkingbd/dplm_150m" |
| dplm = DiffusionProteinLanguageModel.from_pretrained(model_name) |
| ``` |
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| All DPLM checkpoints are available in the table below: |
| | Model size | Num layers | Num parameters | |
| |------------------------------|----|----------| |
| | [dplm_3b](https://huggingface.co/airkingbd/dplm_3b) | 36 | 3B | |
| | [dplm_650m](https://huggingface.co/airkingbd/dplm_650m) | 33 | 650M | |
| | [dplm_150m](https://huggingface.co/airkingbd/dplm_150m) | 30 | 150M | |
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| For details regarding the design space of multimodal protein language models (MPLMs), please refer to our spotlight paper at ICML'25: |
| [Elucidating the Design Space of Multimodal Protein Language Models](https://huggingface.co/papers/2504.11454) |
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| **News**: welcome to check our new work [DPLM-2: A Multimodal Diffusion Protein Language Model](https://huggingface.co/papers/2410.13782), a multimodal protein foundation model that extends DPLM to simultaneously model, understand, and generate both sequences and structures! |
