Transformers
PyTorch
esm
biology
protein-language-model
protein-generation
protein-structure
diffusion
bitwise-modeling
Instructions to use airkingbd/dplm2_bit_650m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use airkingbd/dplm2_bit_650m with Transformers:
# Load model directly from transformers import AutoTokenizer, EsmForDPLM2Bit tokenizer = AutoTokenizer.from_pretrained("airkingbd/dplm2_bit_650m") model = EsmForDPLM2Bit.from_pretrained("airkingbd/dplm2_bit_650m") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16de0a0ec89bf8af60438716bf94ef3ff6bcfefab877326c32b357c1db42fa31
- Size of remote file:
- 2.62 GB
- SHA256:
- 27d73fca4faf9ed5440ae8952c3711c366686d3e5ee49d188bb36cef9d94ff48
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