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"""
LoRA Adapter Merge Script for M1 Mac
Merges LoRA adapters with the base Qwen2.5-1.5B model
"""
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import os
import argparse
def merge_lora_weights(
base_model_name: str = "unsloth/Qwen2.5-1.5B",
lora_adapter_path: str = "fine_tuned_model", #fine_tuned_model
output_path: str = "merged_model",
device: str = "mps" # Use Metal Performance Shaders for M1
):
"""
Merge LoRA adapters with base model
Args:
base_model_name: HuggingFace model name or path to base model
lora_adapter_path: Path to your LoRA adapter files
output_path: Where to save the merged model
device: Device to use ('mps' for M1/M2 Mac, 'cpu' for compatibility)
"""
print(f"🚀 Starting LoRA merge process...")
print(f"📦 Base model: {base_model_name}")
print(f"🔧 LoRA adapters: {lora_adapter_path}")
print(f"💾 Output path: {output_path}")
# Check if MPS is available (M1/M2 Mac)
if device == "mps" and not torch.backends.mps.is_available():
print("⚠️ MPS not available, falling back to CPU")
device = "cpu"
try:
# Step 1: Load the base model
print("\n📥 Loading base model...")
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype=torch.float16 if device == "mps" else torch.float32,
device_map={"": device},
low_cpu_mem_usage=True
)
print("✅ Base model loaded successfully")
# Step 2: Load the tokenizer
print("\n📥 Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(lora_adapter_path)
print("✅ Tokenizer loaded successfully")
# Step 3: Load LoRA adapters
print("\n📥 Loading LoRA adapters...")
model_with_lora = PeftModel.from_pretrained(
base_model,
lora_adapter_path,
device_map={"": device}
)
print("✅ LoRA adapters loaded successfully")
# Step 4: Merge weights
print("\n🔄 Merging LoRA weights into base model...")
merged_model = model_with_lora.merge_and_unload()
print("✅ Weights merged successfully")
# Step 5: Save the merged model
print(f"\n💾 Saving merged model to {output_path}...")
os.makedirs(output_path, exist_ok=True)
merged_model.save_pretrained(
output_path,
safe_serialization=True,
max_shard_size="2GB"
)
tokenizer.save_pretrained(output_path)
print("✅ Merged model saved successfully!")
print(f"\n🎉 Complete! Your merged model is ready at: {output_path}")
print(f"📊 Model size: ~3GB")
# Optional: Print model info
print("\n📋 Model Information:")
print(f" - Architecture: {merged_model.config.architectures}")
print(f" - Parameters: {sum(p.numel() for p in merged_model.parameters()):,}")
print(f" - Vocab size: {merged_model.config.vocab_size}")
return merged_model, tokenizer
except Exception as e:
print(f"\n❌ Error during merge process: {str(e)}")
raise
def main():
parser = argparse.ArgumentParser(description="Merge LoRA adapters with base model")
parser.add_argument(
"--base-model",
type=str,
default="unsloth/Qwen2.5-1.5B",
help="Base model name or path"
)
parser.add_argument(
"--lora-path",
type=str,
default="./fine_tuned_model",
help="Path to LoRA adapter files"
)
parser.add_argument(
"--output-path",
type=str,
default="./merged_model",
help="Output path for merged model"
)
parser.add_argument(
"--device",
type=str,
default="mps",
choices=["mps", "cpu"],
help="Device to use (mps for M1/M2, cpu for compatibility)"
)
args = parser.parse_args()
merge_lora_weights(
base_model_name=args.base_model,
lora_adapter_path=args.lora_path,
output_path=args.output_path,
device=args.device
)
if __name__ == "__main__":
main() |