Convert.py

# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "flash-linear-attention>=0.4.2",
#     "hf-xet>=1.4.3",
#     "huggingface-hub>=1.8.0",
#     "onnx>=1.21.0",
#     "onnx-ir>=0.2.0",
#     "onnxruntime>=1.24.4",
#     "onnxruntime-genai>=0.13.1",
#     "sentencepiece>=0.2.1",
#     "tiktoken>=0.12.0",
#     "torch>=2.11.0",
#     "transformers>=5.4.0",
# ]
# ///

import argparse
from pathlib import Path
from huggingface_hub import snapshot_download
#from onnxruntime_genai.python.models.builder import create_model
from onnxruntime_genai.models.builder import create_model
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--name", required=False,default=None)
    parser.add_argument("--token",required=False)
    args = parser.parse_args()

    token = args.token if args.token else None
    
    pwd = Path.cwd()
    model_dir = pwd / "model"
    onnx_dir = pwd / "onnx"
    cache_dir = pwd / "cache"
    model_dir.mkdir(exist_ok=True)
    onnx_dir.mkdir(exist_ok=True)
    cache_dir.mkdir(exist_ok=True)
    
    # ===== STEP 1: DOWNLOAD (HF HUB + XET backend automatically used) =====
    print(">> Downloading model via huggingface_hub (Xet enabled if installed)...")

    local_path = snapshot_download(
        repo_id=args.name,
        local_dir=str(model_dir),
        token=token
        #local_dir_use_symlinks=False  # important for ONNX tools
    )

    print(f"Model downloaded to: {local_path}")

    # ===== STEP 2: CONVERT USING ONNX GENAI BUILDER =====
    print(">> Converting to ONNX (GenAI format)...")

    create_model(
        model_name=args.name,
        input_path=str(model_dir),     # HF model directory
        output_dir=str(onnx_dir),     # ONNX output
        precision="fp16",              # fp32 | fp16 | int8 | int4 (if supported)
        execution_provider="cpu",      # cpu | cuda | dml
        cache_dir=str(pwd / "cache"),  # optional cache
        extra_options={}
    )

    print("\n✅ Done")
    print(f"ONNX model at: {onnx_dir}")


if __name__ == "__main__":
    main()