Model Import

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Overview

• Model name: Enter a display name for the model (up to 50 characters).
• Base model: Select the base model corresponding to your LoRA model.
• Training method: Select the training method used for the model. Options depend on the selected base model and appear automatically after selection.
• Import source: Only "Import from OSS" is supported. This option is selected by default.
• Bucket: Select the OSS bucket that stores your model files.
• Model directory: Browse and select the directory in the chosen bucket that contains your model checkpoint.
• Model encryption: To protect your data, the platform enables OSS server-side encryption (SSE-OSS) using fully managed keys. The encryption algorithm is AES256.

3. After verifying the information, click OK to submit the import request. To cancel, click Cancel to return to the list page. The system validates the model file format and integrity. If validation passes, the import starts. After completion, view the imported model on the My Models page and deploy, fine-tune incrementally, or delete it. Imported model states include the following: Creating (importing), Created (import succeeded, ready to deploy), Creation Failed (import failed), and Expired (model file changed). The list page displays the following 6 columns: model name/ID (with copy button), base model, source, supported deployment methods, status, and operations. The top-right corner provides Import Model and Return to old version entry points. Use the search box at the top to filter models by name.

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Prerequisites for completing OSS authorization before the first import

{
    "Action": [
        "ram:CreateServiceLinkedRole"
    ],
    "Resource": "*",
    "Effect": "Allow",
    "Condition": {
        "StringEquals": {
            "ram:ServiceName": "datahub.sfm.aliyuncs.com"
        }
    }
}

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Old authorization method upgrade

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OSS product not activated

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Prepare LoRA model files

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Required files and directory structure

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Training parameter constraints

• rank value: rank must be 8, 16, 32, or 64, and all LoRA layers of the same model must use the same rank value; otherwise the model cannot be imported.
• Vocabulary cannot be modified: Models that added new tokens or modified the original vocabulary during training cannot be imported; the vocabulary must exactly match the base model's.
• Chat template cannot be modified: Models that modified the chat_template during training cannot be imported; it must match the base model's default configuration. The chat_template is located in the chat_template field of config.json or tokenizer_config.json.
• Vision models must freeze VIT: Vision-language models must freeze the Vision Transformer part. If the LoRA adapter contains visual-related weight parameters (i.e., VIT not frozen), the model cannot be imported.

from safetensors import safe_open
import argparse

def print_safetensor_structure(file_path):
    print(f"Loading safetensor file: {file_path}")
    print("="*80)

    with safe_open(file_path, framework="pt") as f:
        keys = f.keys()
        print(f"Found {len(keys)} tensors in the file:\n")

        for key in sorted(keys):
            tensor = f.get_tensor(key)
            shape = tuple(tensor.shape)
            dtype = str(tensor.dtype)
            device = tensor.device if hasattr(tensor, 'device') else 'cpu'

            lora_tag = " [LoRA]" if "lora_A" in key or "lora_B" in key else ""

            print(f"[{dtype:>14}] {shape} | {key} {lora_tag}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Print structure of a .safetensors LoRA adapter.")
    parser.add_argument("filepath", type=str, help="Path to the .safetensors file")
    args = parser.parse_args()

    print_safetensor_structure(args.filepath)

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Pre-submission automatic validation

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Import LoRA model

1. On the My Models page, click the "Import Model" button in the upper right corner to enter the import model creation page.
2. Fill in the model information per the table below. The base model must match the base used for LoRA training, the Bucket must have the bailian-datahub-access=read tag added, and after selecting the model directory the system automatically validates the files.
3. After confirming the information, click OK to submit. The system automatically validates the file format and completeness, and starts importing after validation passes; to cancel, click Cancel to return to the list page. After import, view the status at Managing imported models.

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Managing imported models

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Status and flow

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Actions column availability

• Deploy: Only available for the Created state. Clicking it goes to the deployment creation page; for deployment operations, see Deployment and O&M. The button is unavailable for other states or when no deployment method is available.
• Incremental training: Models imported from OSS do not support incremental training; the button is unavailable.
• Delete: Models in the Creating state cannot be deleted; quantized models must be deleted on the model compression page.

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FAQ