Instructions to use airkingbd/dplm_150m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use airkingbd/dplm_150m with Transformers:
# Load model directly from transformers import AutoTokenizer, EsmForDPLM tokenizer = AutoTokenizer.from_pretrained("airkingbd/dplm_150m") model = EsmForDPLM.from_pretrained("airkingbd/dplm_150m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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This card includes the DPLM-150m as described in [Diffusion Language Models Are Versatile Protein Learners.](https://arxiv.org/abs/2402.18567)
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And 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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Code (and usage): https://github.com/bytedance/dplm
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This card includes the DPLM-150m as described in [Diffusion Language Models Are Versatile Protein Learners.](https://arxiv.org/abs/2402.18567)
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And 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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Code (and usage): https://github.com/bytedance/dplm
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