File size: 2,112 Bytes
d863fcd bfe21f8 d863fcd 183b3b6 d863fcd | 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 66 67 | #!/usr/bin/env python3
"""
Simple LoRA merge script.
Usage: python merge_simple.py --base-model Qwen/Qwen2.5-Coder-7B --adapter-path adapters/lora --output-path merged_model
"""
import argparse
import os
from pathlib import Path
import torch
# Disable LoFTQ to avoid bitsandbytes import
os.environ['PEFT_DISABLE_LOFTQ'] = '1'
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--base-model", type=str, required=True, help="Base model name or path")
parser.add_argument("--adapter-path", type=str, required=True, help="LoRA adapter directory")
parser.add_argument("--output-path", type=str, required=True, help="Output directory for merged model")
parser.add_argument("--use-safetensors", action="store_true", help="Use safetensors format")
args = parser.parse_args()
print("="*60)
print("Merging LoRA Adapter")
print("="*60)
print(f"Base model: {args.base_model}")
print(f"Adapter: {args.adapter_path}")
print(f"Output: {args.output_path}")
# Load base model
print("Loading base model...")
model = AutoModelForCausalLM.from_pretrained(
args.base_model,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True)
# Load and merge adapter
print("Loading LoRA adapter...")
model = PeftModel.from_pretrained(model, args.adapter_path)
print("Merging weights...")
model = model.merge_and_unload()
# Save
os.makedirs(args.output_path, exist_ok=True)
print(f"Saving to {args.output_path}...")
model.save_pretrained(args.output_path, safe_serialization=args.use_safetensors)
tokenizer.save_pretrained(args.output_path)
print("="*60)
print("✅ Merge complete!")
print("="*60)
files = list(Path(args.output_path).glob("*"))
print(f"Files saved ({len(files)}):")
for f in files:
print(f" {f.name}")
if __name__ == "__main__":
main()
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