model_auto.py

from transformers import AutoModel
from huggingface_hub import HfApi, snapshot_download

def get_model(model_name: str):
    """
    Load a model from the Hugging Face model hub.

    Args:
        model_name (str): The name of the model to load.

    Returns:
        transformers.PreTrainedModel: The loaded model.
    """
    return AutoModel.from_pretrained(model_name, torch_dtype="bfloat16")


def upload_and_convert(
    model_name: str = "mixedbread-ai/mxbai-rerank-base-v2",
):
    """Upload the converted sequence classifier to the hub."""
    model = get_model(model_name)
    split_name = model_name.split("/")[-1]
    
    

    snapshot_download(f"{model_name}", local_dir=f"./{split_name}")
    model.save_pretrained(f"./{split_name}")

    api = HfApi()
    api.create_repo(repo_id=f"michaelfeil/{split_name}-auto", exist_ok=True)
    api.upload_folder(
        repo_id=f"michaelfeil/{split_name}-auto",
        folder_path=f"./{split_name}",
    )


if __name__ == "__main__":
    upload_and_convert(
        model_name="Qwen/Qwen3-Embedding-0.6B",
    )
    upload_and_convert(
        model_name="Qwen/Qwen3-Embedding-4B",
    )
    upload_and_convert(
        model_name="Qwen/Qwen3-Embedding-8B",
    )
    print("Model uploaded successfully.")