I try running the coding in local ，but give a error about:

e.g. applying modules_to_save to a lora layer makes no sense

217     cls_name = self.original_module.__class__

--> 218 raise TypeError(f"modules_to_save cannot be applied to modules of type {cls_name}")
TypeError: modules_to_save cannot be applied to modules of type <class 'peft.tuners.lora.layer.Linear'>

from transformers import AutoModelForTokenClassification, AutoTokenizer
from peft import PeftModel
import torch

Path to the saved LoRA model

model_path = "/public/home/design2/llm/module/esm2_t33_650M_qlora_binding_16M"

ESM2 base model

base_model_path = "/public/home/design2/llm/module/esm2_t33_650M_UR50D"

Load the model

base_model = AutoModelForTokenClassification.from_pretrained(base_model_path)
loaded_model = PeftModel.from_pretrained(base_model, model_path)

Ensure the model is in evaluation mode

loaded_model.eval()

Load the tokenizer

loaded_tokenizer = AutoTokenizer.from_pretrained(base_model_path)

Protein sequence for inference

protein_sequence = "MAVPETRPNHTIYINNLNEKIKKDELKKSLHAIFSRFGQILDILVSRSLKMRGQAFVIFKEVSSATNALRSMQGFPFYDKPMRIQYAKTDSDIIAKMKGT" # Replace with your actual sequence

Tokenize the sequence

inputs = loaded_tokenizer(protein_sequence, return_tensors="pt", truncation=True, max_length=1024, padding='max_length')

Run the model

with torch.no_grad():
logits = loaded_model(**inputs).logits

Get predictions

tokens = loaded_tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) # Convert input ids back to tokens
predictions = torch.argmax(logits, dim=2)

Define labels

id2label = {
0: "No binding site",
1: "Binding site"
}

Print the predicted labels for each token

for token, prediction in zip(tokens, predictions[0].numpy()):
if token not in ['', '', '']:
print((token, id2label[prediction]))