Add merge+GGUF script

merge_securecoder.py

@@ -0,0 +1,141 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "unsloth",
+#     "transformers>=4.57",
+#     "huggingface_hub",
+# ]
+# ///
+"""Merge the SecureCoder LoRA into the base Qwen3-Coder-30B-A3B-Instruct
+checkpoint, upload a 16-bit safetensors repo, then quantise to Q4_K_M GGUF.
+
+Default base: unsloth/Qwen3-Coder-30B-A3B-Instruct
+Default adapter: Taimwe/securecoder-30b-pro
+
+Run on HF Jobs (a100-large has the headroom to load 30B in 16-bit):
+    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 \\
+         --gguf-repo Taimwe/securecoder-30b-pro-GGUF
+"""
+
+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 + GGUF + 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("--gguf-repo", default=None)
+    p.add_argument("--gguf-quant", default="Q4_K_M")
+    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)
+
+    if args.gguf_repo:
+        log.info("re-loading merged model for GGUF export ...")
+        from unsloth import FastLanguageModel as FLM
+        model, tokenizer = FLM.from_pretrained(
+            model_name=out_dir,
+            max_seq_length=2048,
+            dtype=torch.bfloat16,
+            load_in_4bit=False,
+        )
+        gguf_path = os.path.join(work, "gguf")
+        os.makedirs(gguf_path, exist_ok=True)
+        log.info("quantising to %s ...", args.gguf_quant)
+        try:
+            model.quantize_gguf_model(save_dir=gguf_path, quantization=args.gguf_quant)
+        except Exception as exc:  # noqa: BLE001
+            log.warning("model.quantize_gguf_model failed (%s); falling back to llama-quantize CLI", exc)
+            from huggingface_hub import hf_hub_download
+            from pathlib import Path as _P
+            qbin = hf_hub_download(repo_id="unsloth/llama.cpp", filename="llama-quantize",
+                                   repo_type="model", token=token)
+            import subprocess
+            subprocess.run(["chmod", "+x", qbin], check=False)
+            src = next(_P(out_dir).glob("*.gguf"), None)
+            if src is None:
+                log.error("no GGUF produced by Unsloth quantise pass")
+                return 1
+            subprocess.run([qbin, str(src), str(_P(gguf_path) / src.name), args.gguf_quant], check=True)
+
+        api.create_repo(args.gguf_repo, repo_type="model", exist_ok=True, private=args.private)
+        api.upload_folder(folder_path=gguf_path, repo_id=args.gguf_repo, repo_type="model",
+                          commit_message=f"Add {args.gguf_quant} GGUF export")
+        log.info("GGUF live: https://huggingface.co/%s", args.gguf_repo)
+
+    print("=" * 78)
+    print("MERGE COMPLETE")
+    print(f"  merged: https://huggingface.co/{args.output_repo}")
+    if args.gguf_repo:
+        print(f"  gguf  : https://huggingface.co/{args.gguf_repo}")
+    print("=" * 78)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())