场景：
我曾经使用过fine-tune LLM并使用model.save(model_path)保存它。当我使用ludwig.load(model_path)加载模型时，它完美地加载了，我能够预测测试示例。

描述用例

现在的问题是，我想在Azure Databricks中使用mlflow注册模型，但是它无法找到MLmodel文件。这是因为实际上loaded_model是一个ludwig对象。
代码：

import mlflow

# Start an MLflow run
with mlflow.start_run():
    # Log the model
    mlflow.log_artifact(local_path="/dbfs/FileStore/shared_uploads/..../....._Finetuned_V1.0", artifact_path="model")
    run_id = mlflow.active_run().info.run_id
model_uri = f"runs:/{run_id}/model"
model_name = "...._Finetuned_V1"
result = mlflow.register_model(model_uri=model_uri, name=model_name)

错误：

Registered model '___Finetuned_V1' already exists. Creating a new version of this model...
2023/11/08 21:02:04 INFO mlflow.tracking._model_registry.client: Waiting up to 300 seconds for model version to finish creation. Model name: ____Finetuned_V1, version 18
MlflowException: Model version creation failed for model name: ____Finetuned_V1 version: 18 with status: FAILED_REGISTRATION and message: Failed registration. The source model path must point to a directory containing an MLflow MLmodel, but received: `dbfs:/databricks/mlflow-tracking/64426...../dc14..../artifacts/model/____Finetuned_V1.0`.

请帮助我使用ludwig的方式在mlflow中注册模型，以便我可以继续提供模型服务。如果这个功能不可用，还有什么替代方案吗？