Add library name, pipeline tag and license
#5
by
nielsr
HF Staff
- opened
README.md
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# DPLM
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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.
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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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This repository contains the DPLM model checkpoint of 150M parameters.
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Please refer to our [github repository](https://github.com/bytedance/dplm/tree/main) for code and usage.
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For example, you can load DPLM model as below:
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dplm = DiffusionProteinLanguageModel.from_pretrained(model_name)
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```
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All DPLM checkpoints are available in the table below:
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| Model size | Num layers | Num parameters |
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|------------------------------|----|----------|
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| [dplm_650m](https://huggingface.co/airkingbd/dplm_650m) | 33 | 650M |
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| [dplm_150m](https://huggingface.co/airkingbd/dplm_150m) | 30 | 150M |
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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!
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---
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license: apache-2.0
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pipeline_tag: feature-extraction
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library_name: transformers
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---
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# DPLM
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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.
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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.
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Please refer to our [github repository](https://github.com/bytedance/dplm/tree/main) for code and usage.
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For example, you can load DPLM model as below:
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dplm = DiffusionProteinLanguageModel.from_pretrained(model_name)
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```
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All DPLM checkpoints are available in the table below:
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| Model size | Num layers | Num parameters |
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|------------------------------|----|----------|
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| [dplm_650m](https://huggingface.co/airkingbd/dplm_650m) | 33 | 650M |
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| [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:
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[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!
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