Upload merge_adapter.py with huggingface_hub

merge_adapter.py

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+# /// script
+# dependencies = [
+#     "transformers>=5.14.0",
+#     "peft>=0.19.0",
+#     "torch>=2.5",
+#     "torchvision>=0.20",
+#     "accelerate>=1.0",
+#     "num2words",
+# ]
+# ///
+"""Merge a trained LoRA adapter into base Gemma 4 and push the merged model.
+
+The MLX runtime (gemma4/server.py) can't load PEFT adapters directly, so after
+training we bake the adapter into the base weights and push a standalone model.
+The Mac then converts that to a quantized MLX build:
+
+    # on HF Jobs (this script):
+    merged = base ⊕ adapter  →  push to --merged-repo
+
+    # locally on the Mac afterwards:
+    venus/.venv/bin/python -m mlx_vlm convert \
+        --hf-path khalidFlex/gemma4-gui-agent-merged \
+        --mlx-path ~/.cache/gemma4-gui-agent-mlx-8bit -q --q-bits 8
+"""
+
+import argparse
+
+import torch
+from peft import PeftModel
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--base", default="google/gemma-4-E4B-it")
+    ap.add_argument("--adapter", required=True, help="PEFT adapter repo")
+    ap.add_argument("--merged-repo", required=True, help="where to push the merged model")
+    ap.add_argument("--public", action="store_true")
+    args = ap.parse_args()
+
+    print(f"[merge] loading base {args.base} (bf16, CPU is fine)")
+    model = AutoModelForImageTextToText.from_pretrained(args.base, dtype=torch.bfloat16)
+    processor = AutoProcessor.from_pretrained(args.base)
+
+    print(f"[merge] applying adapter {args.adapter}")
+    model = PeftModel.from_pretrained(model, args.adapter)
+    model = model.merge_and_unload()
+
+    print(f"[merge] pushing merged model to {args.merged_repo}")
+    model.push_to_hub(args.merged_repo, private=not args.public, max_shard_size="4GB")
+    processor.push_to_hub(args.merged_repo, private=not args.public)
+    print("[merge] done.")
+
+
+if __name__ == "__main__":
+    main()