My Models

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Supported Models

• Fine-tuned models: Models fine-tuned in Alibaba Cloud Model Studio. For more information, see Supported Models for Model Fine-tuning.
• Imported models: Models imported from Object Storage Service (OSS). This supports importing models that underwent LoRA fine-tuning or full-parameter fine-tuning.

  Importing full-parameter fine-tuned models is a whitelist feature. To enable it, contact your account manager.

  Model list:
  • China (Beijing)
  • Singapore

  Supported Models	Model Name
  Qwen3	Qwen3-32B
  Qwen3	Qwen3-14B
  Qwen3	Qwen3-8B
  Qwen3	Qwen3-4B-Instruct-2507
  Qwen3-VL	Qwen3-VL-8B-Instruct
  Qwen2.5	Qwen2.5-72B-Instruct
  Qwen2.5	Qwen2.5-32B-Instruct
  Qwen2.5	Qwen2.5-14B-Instruct
  Qwen2.5	Qwen2.5-7B-Instruct
  Qwen2.5-VL	Qwen2.5-VL-72B-Instruct
  Qwen2.5-VL	Qwen2.5-VL-7B-Instruct

  Supported Models	Model Name
  -	-

  Data updates may be delayed. Refer to the actual display on the interface when you import models.

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Import Models

Importing models is not supported through API or command line operations.

1. On the My Models page, click Import Model to open the Import Model interface.
2. Enter a Model Name and select the Base Model (the model to be fine-tuned).
3. For Import Method, select Import from OSS. Currently, no other methods are supported. You need to manually upload the associated files for your LoRA fine-tuned or full-parameter fine-tuned model to Alibaba Cloud Object Storage Service (OSS). Upload View model file examples
   • For the first import, complete the authorization as prompted on the interface and add a tag to the target bucket. For more information, see How to import files from OSS to Alibaba Cloud Model Studio for the first time.
   • Supported OSS bucket storage classes do not include Archive, Cold Archive, or Deep Cold Archive. Encrypted buckets are supported. Private buckets are supported.
   • Accessing files in the root directory of an OSS bucket is not supported. Select an existing subdirectory or create a new one within the OSS bucket for Alibaba Cloud Model Studio to access.
   • You can import model files of any size. After import, they use the free storage space provided by Alibaba Cloud Model Studio.
4. After clicking OK, the system begins importing the model. During peak request times, this process may take longer. You can wait patiently.

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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 Form Fields

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What to do next

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Manage My Models

Delete: Undeploy models before deleting them. If the model comes from Model Fine-tuning, this operation does not delete records from the Model Fine-tuning interface.

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Status and transitions

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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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Invoke My Models

• Key restrictions: After you successfully deploy models fine-tuned in Alibaba Cloud Model Studio, invoke them only using the API key of their workspace. Currently, only DashScope invocation is supported, not (OpenAI compatible) invocation.
• Optimize inference results: When invoking, explicitly configure inference hyperparameters (such as presence_penalty, frequency_penalty, and repetition_penalty). Ensure these settings match the inference hyperparameters used during your offline fine-tuning.

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Model File Examples

• LoRA fine-tuning typically generates two checkpoint files: best_model_checkpoint (the checkpoint with the best performance on the validation set) and last_model_checkpoint (the last saved checkpoint). You can import either one as needed. Ensure the imported model files include adapter_model.safetensors and adapter_config.json. To learn about LoRA fine-tuning methods, refer to the model fine-tuning section in the Alibaba Cloud Large Language Model ACP Course. Examples of model files (checkpoint files) to prepare:
  
• Files to import for full-parameter fine-tuning must be consistent with open source models.

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FAQ

How do I import files from OSS to Alibaba Cloud Model Studio for the first time?

If you import files from OSS to Alibaba Cloud Model Studio for the first time, complete the authorization as prompted on the interface and add the bailian-datahub-access tag to the target OSS bucket before you import.

If you are unfamiliar with the concepts and differences between root accounts and RAM users, first read Permission Management.

• Use a Root Account
• Use a RAM User

1. Click Go to Authorization.
   
2. In the dialog box, click Confirm Authorization. The system automatically enables the OSS Service-linked Role for you (a prerequisite).

   This usually takes effect within seconds. During peak service hours, there may be a slight delay.

   What to do if "This request failed. Try resubmitting or contact the administrator. Error code: 10041495" appears.

   
3. Add the bailian-datahub-access tag to the target OSS bucket.

   This tag marks buckets accessible by Alibaba Cloud Model Studio. Alibaba Cloud Model Studio cannot access untagged buckets.

   1. Go to the OSS Management Console. In the navigation pane on the left, click Bucket List. The buckets that you created are displayed.
   2. In the Tag column for the target bucket, hover over the icon, then click Go to Edit.
      
   3. Click Create Tag.
      
   4. Click Tag. Set the tag name to bailian-datahub-access and the tag value to read. Then, click Save.
      
4. Return to the Import Model interface, reselect the target bucket, and try importing again.

   Note that Alibaba Cloud Model Studio does not support accessing files stored in the root directory of a bucket. Select an existing folder or create a new one within the bucket for Alibaba Cloud Model Studio to access.

1. Click Go to Authorization.
   
2. In the dialog box that appears, click Confirm Authorization. The messages Authorization Failed and The Current User Does Not Have Permission To Create A Service-linked Role are displayed. This error occurs because the current RAM user does not have permission to create a service-linked role. To proceed, you must first grant the RAM user permission to create a service-linked role, and then grant the RAM user permission to access OSS through Alibaba Cloud Model Studio.
   
3. Grant the RAM user permission to create a service-linked role.
   1. Log on to the RAM console as the root account. In the left navigation pane, choose Permission Management > Permission Policies. Then click Create Policy on the interface.
   2. In the Script Editor, you can enter the content from the following script for Effect, Action, Resource, and Condition. Then click OK.

Copy

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

3. After you enter the policy name, click OK.
   
4. In the navigation pane on the left, choose Identity Management > Users. Find the RAM user that you want to authorize. In the Actions column for that user, click Add Permissions.
5. In the permission policy, select the policy you just created (custom policy). Click Confirm New Authorization. The RAM user now has permissions to create a service-linked role.
   
6. Authorize the RAM user to access OSS through Alibaba Cloud Model Studio.
   1. Return to the Import Model interface and click Go to Authorization.
      
   2. In the dialog box, click Confirm Authorization. This action automatically enables the OSS Service-linked Role, which is a prerequisite.

      This usually takes effect within seconds. During peak service hours, there may be a slight delay.

      What to do if "This request failed. Try resubmitting or contact the administrator. Error code: 10041495" appears.

      
7. Add the bailian-datahub-access tag to the target OSS bucket.

   This tag marks buckets accessible by Alibaba Cloud Model Studio. Alibaba Cloud Model Studio cannot access untagged buckets.

   1. Go to the OSS Management Console. In the navigation pane on the left, click Bucket List. View your created buckets.
   2. In the Tag column of the bucket you want to tag, hover over the icon, then click Go to Edit.
      
   3. You can click Create Tag.
      
   4. Click Tag. Add a tag with the tag name bailian-datahub-access and tag value read. Then click Save.
      
8. Return to the Import Model interface. Reselect the target bucket and try importing again.

   Note that Alibaba Cloud Model Studio does not support accessing files stored in the root directory of a bucket. Select an existing folder or create a new one within the bucket for Alibaba Cloud Model Studio to access.