Get fine-tuning job details-Alibaba Cloud Model Studio(Model Studio)-阿里云帮助中心

Retrieves the details of a specific fine-tuning job.

Get fine-tuning job details

In Windows CMD, replace ${DASHSCOPE_API_KEY} with %DASHSCOPE_API_KEY%. In PowerShell, use $env:DASHSCOPE_API_KEY instead.

curl --location --request GET "https://dashscope.aliyuncs.com/api/v1/fine-tunes/<your_fine_tuning_job_id>" \
      --header "Authorization: Bearer ${DASHSCOPE_API_KEY}" \
      --header 'Content-Type: application/json'

Request parameters

Parameter	Type	Location	Required	Description
job_id	String	Path	Yes	The ID of the fine-tuning job to retrieve. This is the `job_id` from the Create fine-tuning job response parameters.

Response examples

Text generation model

{
          "request_id": "c59b2145-a93c-4e00-b610-4d7cc5c521a2",
          "output": {
              "job_id": "ft-202410291653-1c7f",
              "job_name": "ft-202410291653-1c7f",
              "status": "SUCCEEDED",
              "finetuned_output": "qwen3-14b-suffix-ft-202410291653-1c7f",
              "model": "qwen3-14b",
              "base_model": "qwen3-14b",
              "training_file_ids": [
                  "976bd01a-f30b-4414-86fd-50c54486e3ef"
              ],
              "validation_file_ids": [],
              "hyper_parameters": {
                  "n_epochs": 3,
                  "batch_size": 32,
                  "max_length": 8192,
                  "learning_rate": "1.6e-5",
                  "lr_scheduler_type": "linear",
                  "split": 0.9
              },
              "training_type": "sft",
              "create_time": "2024-10-29 16:53:53",
              "workspace_id":"llm-v71tlv***",
              "user_identity": "1396993924585947",
              "modifier": "1396993924585947",
              "creator": "1396993924585947",
              "end_time": "2024-10-29 17:11:26",
              "group": "llm",
              "usage": 279808
          }
      }

Video generation model

Note the output.status, where SUCCEEDED indicates that the training is complete, and output.usage, which is the total number of tokens consumed during training.

{
    "request_id": "9bbb953c-bef2-4b59-9fc5-xxxxxxxxx",
    "output": {
        "job_id": "ft-202511111122-xxxx",
        "status": "SUCCEEDED",
        "finetuned_output": "wan2.5-i2v-preview-ft-202511111122-xxxx",
        "model": "wan2.5-i2v-preview",
        "base_model": "wan2.5-i2v-preview",
        "training_file_ids": ["xxxxxxxxxxxx"],
        "validation_file_ids": [],
        "hyper_parameters": {
            "n_epochs": 400,
            "learning_rate": 2.0E-5,
            "split": 0.9,
            "eval_epochs": 50
        },
        "training_type": "efficient_sft",
        "create_time": "2025-11-11 11:22:22",
        "end_time": "2025-11-11 16:49:01",
        "usage": 432000,
        "output_cnt": 8
    }
}

Image generation model

{
    "request_id": "03d738f5-3720-90b0-9c7b-xxxxxxxxx",
    "output": {
        "job_id": "ft-202606030110-xxxx",
        "status": "SUCCEEDED",
        "finetuned_output": "wan2.7-image-pro-ft-202606030110-xxxx",
        "model": "wan2.7-image-pro",
        "base_model": "wan2.7-image-pro",
        "training_file_ids": ["xxxxxxxxxxxx"],
        "validation_file_ids": [],
        "hyper_parameters": {
            "max_steps": 800,
            "learning_rate": 3.0E-5,
            "eval_steps": 200,
            "max_token_length": "2k",
            "max_pixels": "2k",
            "val_img_size": "2k",
            "generation_type": "t2i",
            "lora_rank": 32,
            "lora_alpha": 32
        },
        "training_type": "efficient_sft",
        "create_time": "2026-06-03 01:10:47",
        "end_time": "2026-06-03 01:38:53",
        "usage": 10273216,
        "output_cnt": 1
    }
}

Response parameters

Parameter	Type	Description
request_id	String	The unique ID for this request.
output	Object	The details of the fine-tuning job.
output.job_id	String	The ID of the fine-tuning job, used to query its status. Generation rule: `ft-{yyyyMMddHHmm}-{4-digit UUID}`.
output.job_name	String	Same as `output.job_id`.
output.status	String	The task status of this fine-tuning job.
output.finetuned_output	String	This field is returned only when the task status is SUCCEEDED.
output.model	String	The ID of the model used for the fine-tuning job.
output.base_model	String	The ID of the base model used for this fine-tuning job. For example, the fine-tuning job `ft-202410291653-1c7f` uses the base model `qwen3-14b`.
output.training_file_ids	Array	A list of training file IDs.
output.validation_file_ids	Array	A list of validation file IDs.
output.hyper_parameters	Object	A map of explicitly specified hyperparameters.
output.training_type	String	The fine-tuning method.
output.create_time	String	The creation time of the fine-tuning job.
output.workspace_id	String	The ID of the workspace that contains the fine-tuning job.
output.user_identity	String	The UID of the main account that owns this fine-tuning job.
output.modifier	String	The UID of the account that last modified this fine-tuning job. For example, if a sub-account cancels the job, the UID of that sub-account is displayed here.
output.creator	String	The UID of the account that created this fine-tuning job.
output.end_time	String	The time the fine-tuning job ended. This field is returned when the task status is `SUCCEEDED`, `FAILED`, or `CANCELED`.
output.group	String	The type of the fine-tuning job.
output.usage	Integer	The number of tokens consumed by the fine-tuning job. For the billing formula, see billable items. This field is returned when the task status is SUCCEEDED or CANCELED.
output.output_cnt	Integer	The number of checkpoints generated so far. This parameter is returned only for models that support multiple checkpoint outputs (for example, `cosyvoice-v3-flash`). To get the full list of checkpoints, call the List checkpoints API.
output.max_output_cnt	Integer	The maximum number of checkpoints that can be produced in a single task. The part of `output_cnt` that exceeds this limit is truncated based on tuning sufficiency. This parameter is returned only for models that support multiple checkpoint outputs.

Error codes

Returned when the request fails.

Field	Type	Description	Example value
code	String	The error code.	NotFound
request_id	String	The system-generated unique ID of the request.	6332fb02-3111-43f0-bf79-f9e8c5ffa7f9
message	String	The error message.	Not Found!

Example error response

{
        "code": "NotFound",
        "request_id": "BE213CDD-8A5C-59EE-9A67-055EAB0CB59B",
        "message": "Not Found!"
      }

Error code list

HTTP status code	Error code	Example message	Description	Solution
400	InvalidParameter	Missing training files	A parameter is invalid, missing, or in the wrong format.	Check the error message and correct the parameters in your request.
400	UnsupportedOperation	The fine-tuning job cannot be deleted because its status is SUCCEEDED, FAILED, or CANCELED.	The operation cannot be performed because the resource is in a specific state.	Wait until the resource reaches an actionable state before retrying the operation.
404	NotFound	Not found!	The requested resource does not exist.	Verify that the ID of the resource you are trying to access is correct.
409	Conflict	Model instance 'xxxxx' already exists. Please specify a suffix.	A deployment instance with the name 'xxxxx' already exists. You must specify a suffix to make the name unique.	Specify a unique suffix for the deployment.
429	Throttling	Too many fine-tuning jobs are currently running. Please try again later. A user can have a maximum of 20 fine-tuning jobs in a 'running' or 'succeeded' state.	The request was denied due to platform limits.	Delete unused models. To increase the concurrency for fine-tuning jobs or retain more successfully fine-tuned models, contact your account manager.
500	InternalError	Internal server error!	An internal server error occurred.	Record the request_id and submit a ticket for assistance.