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