File size: 2,206 Bytes
8371fea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | #!/usr/bin/env python3
"""
Example usage script for LOL-EVE model.
This script demonstrates how to load and use the LOL-EVE model for genomic sequence analysis.
"""
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
def main():
print("🧬 LOL-EVE Example Usage")
print("=" * 40)
# Load model and tokenizer
print("Loading model and tokenizer...")
tokenizer = AutoTokenizer.from_pretrained('Marks-lab/LOL-EVE')
model = AutoModelForCausalLM.from_pretrained('Marks-lab/LOL-EVE', trust_remote_code=True)
print("✅ Model loaded successfully!")
# Example 1: Basic DNA sequence
print("\n1. Basic DNA Sequence Analysis")
print("-" * 30)
basic_sequence = "[MASK] [MASK] [MASK] [SOS]ATGCTAGCTAGCTAGCTAGCTA[EOS]"
print(f"Input: {basic_sequence}")
inputs = tokenizer(basic_sequence, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
print(f"Output shape: {outputs.logits.shape}")
print(f"Sequence length: {outputs.logits.shape[1]} tokens")
# Example 2: Control code sequence (recommended)
print("\n2. Control Code Sequence Analysis")
print("-" * 30)
control_sequence = "brca1 human primate [SOS] ATGCTAGCTAGCTAGCTAGCTA [EOS]"
print(f"Input: {control_sequence}")
inputs = tokenizer(control_sequence, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
print(f"Output shape: {outputs.logits.shape}")
print(f"Sequence length: {outputs.logits.shape[1]} tokens")
# Example 3: Different gene
print("\n3. Different Gene Analysis")
print("-" * 30)
tp53_sequence = "tp53 human primate [SOS] GATCGATCGATCGATCGATCGA [EOS]"
print(f"Input: {tp53_sequence}")
inputs = tokenizer(tp53_sequence, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
print(f"Output shape: {outputs.logits.shape}")
print(f"Sequence length: {outputs.logits.shape[1]} tokens")
print("\n" + "=" * 40)
print("🎉 All examples completed successfully!")
print("The model is ready for your genomic analysis tasks.")
if __name__ == "__main__":
main()
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