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| """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 |
|
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|
| 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.") |
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|
| if __name__ == "__main__": |
| main() |
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|
