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dnagpt/
dna_promoter_sft

Name: dna_promoter_sft
Availability: InStock

safetensors

gpt2

mit

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brief

DNA sequence and Human Language mixed large language model with fine tuning

You can use 'chat' to conduct bioinformatics research：)

basic use

python

import torch
from transformers import GPT2Tokenizer,GPT2Model,GPT2LMHeadModel

model = GPT2LMHeadModel.from_pretrained("dnagpt/dna_promoter_sft")
tokenizer = GPT2Tokenizer.from_pretrained("dnagpt/dna_eng_bpe")

input = """
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Determine the following dna sequence is promoter or terminator

### Input:
TCTTTCTCTTCTGTATCATTCTACTTCTATGACTGCTCCTTCTCGAGTAAAACAGAATGTGTCTCAGGATTACTTTAAAACAAGACAAAGTATAGAGTTAAAATACATTTTTGTTTAGGTAAATATCATCAAATAATCCAATTTGGAAACCAACATTCTTACTTCTTTTTCCAACAGTTGTTCCTATCATCATAAAAACAGAGAGGGTAAGCCTTGGGGAAAGCTACTTTAAAAAAATGGCCTCTAAGGATATTCAGGGTGCAAACAGTAACCTGTTCAGGCACAGATTCTTCTCCTTGAAATGAACTGAAGTTGGCAAATATTCCTCACAGCCTGTTGGAGGGTTCAGCAGTTTATTACAGAAGTATGAAATGCTTTTATTTAAAAAATGTATTTTGGTACACATGTTAGTCTTTTTTGTGATGAACTTCACAGATACATTTGACATTGGTATGCCTCATTTATTTGTTGAAATTTTTTTCTTTGGCTTCCATGAAGTTAAGATTTTTCAAGCAAGATGAAGTCCATCATCCTCTTTGTCCTTTCCCTGCTCCTTATCTTGGAGAAGCAAGCAGCTGTGATGGGACAAAAAGGTGAGTGATGGTTTCAGCTGGACTCCATCCTTCAACTGACAACCCAACACAACTGTATTCATCTCTCGTTAACATTACTAGCAGTGAGTAACATCAGAAAGTTTTTGAGTCTCCTTCCTACTCCGGACTGAATTTCTTCTGTATAATGCAAGCGATCTGGCATGATGATATACAAAGACCGATAAAATTTTGCTGGGGATTCTGAAAGTAAAAAAAATTGCCTTTGATATTATGTCCCCATGCTAAGTCCCTGGGGACTTTGACATTATCCCCCACTGAGCAGGGGTGAGGAAGCTGGCATTTACTAAAAAGTCAGCAATATGATTTGAATGCCCTACATAAGGCGACAAAATCAAAACAACACCTAGGTGGAAGTCAACAACTGCTCAATTATAAACAAGAAGGCAATAAGCTATGAAAGGGCAGTGCCTTTTGACATTTCAGCTCCACCCATAGCACACCCACTCAAGGAACATATAAATGAAGAGATCCGCTCAGTTCTCAGACCTATTTTGGACAAAAAGACCAACAACATACTAAATCCAAAGGCAGTTTTTCTATTCAACACACATATCATGTAGACATCAATGATCATGACTGGACCCGATGAGAATTGATTTTTCTCCACCCAACGCTGTAGGCTTTTGGAAATATCAGAAATTTGTTGGGAAAAGGTGGGAGGTAAGAGTTGCAAGAGAGCTTTGGAGATAATGAATGCATACATTTCTATTATCAATTACCAGGTGGATCAAAAGGCCAATTGCCAAGCGGATCTTCCCAATTTCCACATGGACAAAAGGGCCAGCATACCTAAAATTTAGTATTTAAGTTATTGGATCAGAAAGGAAACTCGCATTTAGAGTATGAAGGCATTGTCAGCCACCAATTACTTTTGTAACCTGAAGCTGAATAGCCCATCTGGAAAGGGATTATCCAGTCAATGTTCAAACACAGAAAAAAGGCTATGGGTTCATGGACTAAGTAAAGAACAAGCTTCAGCCTCTGGTGAGACCATGATAAATCAAAAGGTCATTTTCACATGATAGTTATACATCATAAAGGAGGCCAAGCTCATCATGGGACACAAAATCCTTCTCAAGATCAGGGTGTTAAATTTTTCTCATCCTTTCAAAAAATCTCTGGAGCTTACCTCACCCTCCAGATACAGCCTCACCTCTCATTCCACCATGAAATCAGACTTCTTGAGGCACAAAAAGGTAGAACACAAGGTGGATCCCAAAGCAGTTATGTTCTCCAAACTGAAGAACTAGTAGTTAACAAACAACAACGTGAGACTAAAAATTCTCATCAAAATAAAGGGCATTACCAAAATGTGGTTGACGTGAGAGAGGAACATTCAAGTAAACTACAAACTTCACTCCATCCTGCACATCAAGACAGACTCCA

### Response:
"""

input_ids = tokenizer.encode(
      input,
      return_tensors="pt",
      truncation=True,
      max_length=1000
)

# Generate
device = model.device

generated_tokens_with_prompt = model.generate(
input_ids=input_ids.to(device),
max_new_tokens=3,
)

generated_text_with_prompt = tokenizer.decode(generated_tokens_with_prompt[0], skip_special_tokens=True)
generated_text_answer = generated_text_with_prompt[len(input):]

print(generated_text_answer)

#terminator

github

https://github.com/maris205/DNAHL