merge_model.py

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

# print("Loading base model...")
# base_model = AutoModelForCausalLM.from_pretrained(
#     "./models/LFM2-1.2B",
#     torch_dtype=torch.bfloat16,
#     device_map="auto",
#     trust_remote_code=True
# )

# print("Loading LoRA adapters...")
# model = PeftModel.from_pretrained(base_model, "./counselor_model/final_model")

# print("Merging adapters with base model...")
# merged_model = model.merge_and_unload()

# print("Saving merged model...")
# merged_model.save_pretrained("./counselor_model-merged", safe_serialization=True)

# tokenizer = AutoTokenizer.from_pretrained("./models/LFM2-1.2B")
# tokenizer.save_pretrained("./counselor_model-merged")

# print("Model merge complete!")

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

def merge_and_save_model(
    base_model_name: str = "LiquidAI/LFM2-2.6B",
    adapter_path: str = "./lfm_minimal_output/final_model",
    output_path: str = "./merged_counselor_minimal_2b"
):
    """
    Properly merge LoRA weights with base model
    """
    print("Loading base model...")
    # Load the base model
    base_model = AutoModelForCausalLM.from_pretrained(
        base_model_name,
        torch_dtype=torch.float16,
        device_map="auto",
        trust_remote_code=True
    )
    
    print("Loading LoRA adapter...")
    # Load the PEFT model (LoRA adapter)
    model = PeftModel.from_pretrained(
        base_model,
        adapter_path,
        torch_dtype=torch.float16,
    )
    
    print("Merging weights...")
    # Merge LoRA weights with base model
    model = model.merge_and_unload()
    
    print(f"Saving merged model to {output_path}...")
    # Save the merged model
    model.save_pretrained(output_path)
    
    # Also save the tokenizer
    tokenizer = AutoTokenizer.from_pretrained(adapter_path)
    tokenizer.save_pretrained(output_path)
    
    print("✅ Model merged and saved successfully!")
    return model, tokenizer

# Run the merge
if __name__ == "__main__":
    merge_and_save_model()