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

# Load the base model and tokenizer
base_model_id = "RiverTest/autotrain-uiny8-3o6jx" # Replace with your base model
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(base_model_id)

# Load the fine-tuned model
ft_model_id = "RiverTest/TrainerToMerge" # Replace with your fine-tuned model
ft_model = PeftModel.from_pretrained(base_model, ft_model_id)

merged_model = ft_model.merge_and_unload()

# Specify the folder path where you want to save the merged model
output_folder = '.' # Replace 'yourFolder' with your desired folder name

# Save the merged model to the specified folder
merged_model.save_pretrained(output_folder)