changed the use of finetune

app/model_handler.py

@@ -4,7 +4,8 @@ from .utils import format_prompt_for_hint, format_prompt_for_followup, format_pr
 from peft import PeftModel, PeftConfig
 
 MODEL_NAME = "Salesforce/codegen-350M-mono"
-LORA_PATH = "C:/Users/yaswa/OneDrive/Desktop/New folder/Fine-tuning-data/fine-tuned-model"
+# Use a relative path for the LoRA model, assuming it's in the project root
+LORA_PATH = "./fine-tuned-model"
 
 # Initialize the tokenizer
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
@@ -25,7 +26,7 @@ model = PeftModel.from_pretrained(
     device_map="auto"
 )
 
-# Merge LoRA weights with base model for better inference performance (optional)
+# Merge LoRA weights with base model for better inference performance
 model = model.merge_and_unload()
 
 def generate_hint(code_snippet, task_description, mode='concise'):