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# requires-python = ">=3.10"
# dependencies = [
# "unsloth",
# "transformers>=4.57",
# "huggingface_hub",
# ]
# ///
"""Merge the SecureCoder LoRA into the base Qwen3-Coder-30B-A3B-Instruct
checkpoint and upload a 16-bit safetensors repo. GGUF is done in a separate
job (the 30B-A3B MoE does not fit on one 80 GB card when merge and quantise
both run in the same process).
Default base: unsloth/Qwen3-Coder-30B-A3B-Instruct
Default adapter: Taimwe/securecoder-30b-pro
Run on HF Jobs:
hf jobs run -d --flavor a100-large --timeout 90m --secrets HF_TOKEN \\
ghcr.io/astral-sh/uv:python3.12-bookworm \\
uv run --no-project https://huggingface.co/Taimwe/securecoder-scripts/resolve/main/merge_securecoder.py \\
-- --adapter Taimwe/securecoder-30b-pro \\
--output-repo Taimwe/securecoder-30b-pro-merged
"""
from __future__ import annotations
import argparse
import logging
import os
import shutil
import sys
import time
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger("merge")
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description="Merge + push the SecureCoder LoRA")
p.add_argument("--base", default="unsloth/Qwen3-Coder-30B-A3B-Instruct")
p.add_argument("--adapter", default="Taimwe/securecoder-30b-pro")
p.add_argument("--output-repo", default="Taimwe/securecoder-30b-pro-merged")
p.add_argument("--private", action="store_true")
p.add_argument("--work-dir", default="/data/securecoder-merge")
p.add_argument("--max-shard-size", default="5GB")
return p.parse_args()
def main() -> int:
args = parse_args()
token = os.environ.get("HF_TOKEN")
if not token:
log.error("HF_TOKEN not set")
return 1
import torch
from huggingface_hub import HfApi
from unsloth import FastLanguageModel
if not torch.cuda.is_available():
log.error("no CUDA - merge needs a GPU")
return 1
log.info("GPU: %s", torch.cuda.get_device_name(0))
work = args.work_dir
if os.path.exists(work):
shutil.rmtree(work)
os.makedirs(work, exist_ok=True)
log.info("loading base %s in 16-bit ...", args.base)
started = time.time()
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=args.base,
max_seq_length=2048,
dtype=torch.bfloat16,
load_in_4bit=False,
)
log.info("loading adapter %s ...", args.adapter)
from peft import PeftModel
model = PeftModel.from_pretrained(model, args.adapter, token=token)
log.info("merging ...")
model = model.merge_and_unload()
log.info("merge done in %.1f min", (time.time() - started) / 60)
out_dir = os.path.join(work, "merged")
model.save_pretrained(out_dir, safe_serialization=True, max_shard_size=args.max_shard_size)
tokenizer.save_pretrained(out_dir)
log.info("saved merged model to %s", out_dir)
api = HfApi(token=token)
api.create_repo(args.output_repo, repo_type="model", exist_ok=True, private=args.private)
log.info("uploading to %s ...", args.output_repo)
api.upload_folder(folder_path=out_dir, repo_id=args.output_repo, repo_type="model",
commit_message="Merge SecureCoder LoRA into base (16-bit)")
log.info("merged model live: https://huggingface.co/%s", args.output_repo)
print("=" * 78)
print("MERGE COMPLETE")
print(f" merged: https://huggingface.co/{args.output_repo}")
print(" next : run quantise_securecoder.py separately for GGUF")
print("=" * 78)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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