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lamm-mit/
GPTProteinPretrained

Name: GPTProteinPretrained
Rating: 2.5 (10 reviews)

text-generation-inference

endpoints_compatible

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Pretrained model

This model is a pretrained autoregressive transformer model in GPT-style, trained on a large number of protein sequences. The training task used is "Sequence<...>".

Dataset: https://huggingface.co/datasets/lamm-mit/GPTProteinPretrained

Load pretrained model:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

pretrained_model_name='lamm-mit/GPTProteinPretrained'

tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

model_name = pretrained_model_name
 
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    trust_remote_code=True
).to(device)

model.config.use_cache = False

Sample inference using the "Sequence<...>" task, where here, the model will simply autocomplete the sequence starting with "AIIAA":

python

import torch 
device='cuda'
prompt = "Sequence<ETAVPKLLQAL"
generated = torch.tensor(tokenizer.encode(prompt, add_special_tokens = False)) .unsqueeze(0).to(device)
print(generated.shape, generated)

sample_outputs = model.generate(
                                inputs=generated, 
                                eos_token_id =tokenizer.eos_token_id,
                                do_sample=True,   
                                top_k=500, 
                                max_length = 1024,
                                top_p=0.9, 
                                num_return_sequences=1,
                                temperature=1,
                                ).to(device)

for i, sample_output in enumerate(sample_outputs):
      print("{}: {}\n\n".format(i, tokenizer.decode(sample_output, skip_special_tokens=True)))

Output (here, three candidate sequences):

raw

torch.Size([1, 57]) tensor([[ 86, 104, 116, 120, 104, 113, 102, 104,  63,  80,  74,  84,  72,  73,
          89,  81,  84,  87,  90,  89,  81,  72,  73,  76,  79,  79,  74,  79,
          86,  86,  71,  84,  81,  87,  84,  89,  73,  79,  73,  89,  79,  76,
          79,  89,  80,  92,  76,  76,  87,  89,  89,  74,  81,  86,  79,  76,
          79]], device='cuda:0')
0: Sequence<MGQEFVNQTWVNEFILLGLSSDQNTQVFLFVLILVMYIITVVGNSLILLLIRLDSRLHTPMYFFLSNLSFVDLCFSTTTVPQLLANFLSVHKSISFLGCVAQLYIFLTLGGTEFFLLGAMAYDRYVAVCYPLHYTVIMNWRVCTSLAVASWVSGFLNSLVHTVITFRLPFCGPNEIDHFFCEVPALLKLACADTSLNEMAMNACCVLILLIPFSLILISYTRILITILRMPSATGRRKAFSTCASHIIVVILFYGTAISTYIQPSSDPVADQDKLMALFYAILTPMLNPIIYSLRNKDVKGAWQKLLNKLRVTQKRKFMAVTLH>

Citation

To cite this work:

@article{WeiKaplanBuehler_2023,
    title   = {Generative pretrained autoregressive transformer graph neural network applied to the analysis and discovery of novel proteins},
    author  = {M.J. Buehler},
    journal = {J. Appl. Phys.},
    year    = {2023},
    volume  = {},
    pages   = {},
    url     = {https://doi.org/10.1063/5.0157367}
}