File size: 2,841 Bytes
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Merge a LoRA / QLoRA adapter into a base model at FP16 precision and save the
result as a standalone HuggingFace-format model directory.
Rationale:
The old restored-inference path (scripts/run_inference_restored.py) loads
the base in BnB-NF4, then calls PeftModel.merge_and_unload() on the 4-bit
weights. PEFT itself warns that this merge introduces rounding errors:
"Merge lora module to 4-bit linear may get different generations due
to rounding errors."
On top of that, HuggingFace `.generate()` runs one sample at a time, which
is ~20-50x slower than vLLM's batched PagedAttention for a small model.
This script merges the adapter into FP16 (which is lossless) and writes a
standalone model. Downstream, run_inference.py can load the merged model
via vLLM and (re-)quantize to NF4 at load time — giving the same target
deployment (a 4-bit quantized, adapter-baked model) with dramatically
better throughput and one fewer quantization round-trip.
"""
import argparse
import os
def main():
parser = argparse.ArgumentParser(description="Merge LoRA adapter into FP16 base and save.")
parser.add_argument("--model", required=True, help="Base model HF name or local path")
parser.add_argument("--adapter", required=True, help="LoRA adapter directory")
parser.add_argument("--output", required=True, help="Output directory for merged model")
parser.add_argument("--dtype", default="bfloat16", choices=["float16", "bfloat16"],
help="Precision for the merged model on disk")
args = parser.parse_args()
if os.path.exists(os.path.join(args.output, "config.json")):
print(f"[SKIP] Merged model already exists at: {args.output}")
return
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[args.dtype]
print(f"Loading base model in {args.dtype}: {args.model}")
model = AutoModelForCausalLM.from_pretrained(
args.model,
torch_dtype=dtype,
device_map="auto",
trust_remote_code=True,
)
print(f"Applying adapter: {args.adapter}")
model = PeftModel.from_pretrained(model, args.adapter)
print("Merging adapter into base weights")
model = model.merge_and_unload()
os.makedirs(args.output, exist_ok=True)
print(f"Saving merged model to: {args.output}")
model.save_pretrained(args.output, safe_serialization=True)
tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
tokenizer.save_pretrained(args.output)
if torch.cuda.is_available():
print(f"Peak GPU memory: {torch.cuda.max_memory_allocated() / 1e9:.1f} GB")
print("Done.")
if __name__ == "__main__":
main()
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