Create a tuning job

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Create a model fine-tuning task.

Create a fine-tuning job

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

Text generation

Video and image generation

curl --location --request POST "https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes" \
      --header "Authorization: Bearer ${DASHSCOPE_API_KEY}" \
      --header 'Content-Type: application/json' \
      --data '{
          "model":"qwen3-14b",
          "training_file_ids":[\
              "86a9fe7f-dd77-43b0-9834-2170e12339ec",\
              "03ead352-6190-4328-8016-61821c23d4fc"\
          ],
          "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",
          "finetuned_output_suffix":"suffix"
      }'

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes' \
--header "Authorization: Bearer ${DASHSCOPE_API_KEY}" \
--header 'Content-Type: application/json' \
--data '{
    "model": "wan2.7-i2v",
    "training_file_ids": [\
        "<Replace with the file ID of your training dataset>"\
    ],
    "training_type": "efficient_sft",
    "hyper_parameters": {
        "n_epochs": 400,
        "batch_size": 1,
        "learning_rate": 2e-5,
        "split": 0.9,
        "max_split_val_dataset_sample": 5,
        "eval_epochs": 50,
        "max_pixels": 147456,
        "save_total_limit": 10,
        "lora_rank": 32,
        "lora_alpha": 32
    }
}'

Input parameters

Parameter	Required	Type	Location	Description

Parameter	Required	Type	Location	Description
training_file_ids	Yes	Array	Body	A list of file IDs for the training set. File IDs are generated by the File Management API.
validation_file_ids	No	Array	Body	A list of file IDs for the validation set. File IDs are generated by the File Management API.
model	Yes	String	Body	The ID of the model to fine-tune. This can be a or the ID of a model from a previous tuning job. Video/image generation models support the following model values: - Image-to-Video - Based on First Frame: wan2.7-i2v (recommended), wan2.6-i2v, wan2.5-i2v-preview, wan2.2-i2v-flash - Image-to-Video - Based on First and Last Frame: wan2.2-kf2v-flash
hyper_parameters	No	Map	Body	An object that contains the hyperparameters for the tuning job. The supported parameters and their default values vary by model. To view the actual default values, select the corresponding model and tuning method in the console. - For text generation, visual understanding, and similar models: Use parameters such as n_epochs (number of epochs), batch_size (batch size), and max_length (sequence length). The n_epochs, batch_size, and max_length parameters affect tuning costs and are required. For details about each parameter, see hyper_parameters Description. - CosyVoice speech synthesis model (cosyvoice-v3-flash only): All eight LM/FM hyperparameters are required. For details about each parameter, see "CosyVoice speech synthesis model hyper_parameters" below.
training_type	No	String	Body	The tuning method. Valid values are sft or efficient_sft. Video/image generation models only support efficient_sft (LoRA efficient fine-tuning).
job_name	No	String	Body	A name for the tuning job.
model_name	No	String	Body	The name of the resulting fine-tuned model.

Text generation model

Hyperparameters for video generation models

hyper_parameters supported settings

Parameter	Default	Recommended settings	Type	Description
n_epochs	1	Adjust based on fine-tuning results.	Integer	Number of times the model iterates through the training data. Higher values increase training duration and cost.
learning_rate	- sft: 1e-5 level - efficient_sft: 1e-4 level The specific value varies depending on the selected model.	Use the default value.	Float	Controls the intensity of model weight updates. - Too high: parameters change drastically, degrading performance. - Too low: performance may not change significantly.
freeze_vit	true	Adjust as needed.	Boolean	Freezes the visual backbone parameters so that its weights remain unchanged during training. Applies only to Qwen-VL models.
batch_size	The specific value varies depending on the selected model. The larger the model, the smaller the default batch size.	Use the default value.	Integer	Number of data entries per training iteration. Smaller values prolong training time.
eval_steps	50	Adjust as needed.	Integer	Interval (in steps) for evaluating training accuracy and loss during training. Controls display frequency of Validation Loss and Token Accuracy.
logging_steps	5	Adjust as needed.	Integer	Interval (in steps) for printing fine-tuning logs.
lr_scheduler_type	cosine	Recommended: linear or Inverse_sqrt	String	Strategy for dynamically adjusting the learning rate during training. Valid values:
max_length	2048	8192	Integer	Maximum token length per training entry. Entries exceeding this limit are discarded.
max_split_val_dataset_sample	1000	Use the default value.	Integer	If "validation_file_ids" is not set, Alibaba Cloud Model Studio automatically splits a validation set of up to 1,000 entries. If you set "validation_file_ids", this parameter is ignored.
split	0.8	Use the default value.	Float	If you do not set "validation_file_ids", Model Studio automatically uses 80% of the training file as the training set and 20% as the validation set. If you set "validation_file_ids", this parameter has no effect.
warmup_ratio	0.05	Use the default value.	Float	Proportion of total training steps dedicated to learning rate warmup. During warmup, the learning rate linearly increases from a small initial value to the configured rate. Limits the extent of parameter changes during early training, improving stability. Too high: equivalent to a low learning rate; performance may not change. Too low: equivalent to a high learning rate; may degrade performance. > Does not apply to the "Constant" learning rate scheduler type.
weight_decay	0.1	Use the default value.	Float	L2 regularization strength. Helps maintain model generalization. If too high, fine-tuning effects are insignificant.
Parameters for efficient SFT (supportsefficient_sft ) Note When you perform a second round of efficient fine-tuning on a model that has already been efficiently fine-tuned, the lora_rank, lora_alpha, and lora_dropout parameters must be consistent.
lora_rank	8	64	Integer	Rank of the low-rank matrix in LoRA. Higher ranks improve fine-tuning results but slightly slow down training.
lora_alpha	32	Use the default value.	Integer	Scaling factor that controls the balance between original model weights and LoRA corrections. Larger values give more weight to LoRA corrections, making the model more task-specific. Smaller values preserve more pre-trained model knowledge.
lora_dropout	0.1	Use the default value.	Float	Dropout rate for LoRA low-rank matrix values. The recommended value enhances generalization. If too high, fine-tuning effects are insignificant.
Parameters for publishing checkpoints
save_strategy	epoch	Can be set to epoch or steps. - When set to steps, set the save_steps parameter to adjust the saving interval.	String	Controls the interval and maximum number of checkpoints saved during fine-tuning.
save_steps	50	To modify it, set it to an integer multiple of the eval_steps parameter.	Integer	Number of training steps after which a checkpoint is saved.
save_total_limit	1	10	Integer	Maximum number of checkpoints to save for export.

These hyperparameters apply only to video generation models (Wan series). If model performance is poor or training fails to converge, consider adjusting n_epochs or learning_rate. A minimum of 800 total training steps is recommended.

Parameter	Type	Required	Description	Recommended value
batch_size	int	Yes	Batch size. The number of data samples processed in a single training iteration. - wan2.7-i2v: Recommended 1. - wan2.6-i2v: Recommended 1. - wan2.5-i2v-preview: Recommended 2. - wan2.2-i2v-flash: Recommended 4. - wan2.2-kf2v-flash: Recommended 4.	Varies by model
n_epochs	int	Yes	Number of training epochs. The total number of steps is calculated as: steps = n_epochs × ⌈dataset size / batch_size⌉. A minimum of 800 total steps is recommended. > Example: If the dataset has 5 samples and batch_size = 2, the steps per epoch = ⌈5/2⌉ = 3. The minimum n_epochs would be 800/3 ≈ 267.	400
learning_rate	float	Yes	Learning rate. Controls the magnitude of model weight updates during training. A value that is too high can degrade model performance, while a value that is too low may result in insignificant changes.	2e-5
eval_epochs	int	Yes	Validation interval. The interval, in epochs, at which to perform validation and save a checkpoint. The value must be ≥ n_epochs/10.	50
max_pixels	int	Yes	Maximum resolution for training videos (total pixels = width × height). The system only resizes videos that exceed this value. - wan2.7-i2v: Recommended 147456. Range: 36864–147456. - wan2.6-i2v: Recommended 36864. Range: 16384–36864. - wan2.5-i2v-preview: Recommended 36864. Range: 16384–36864. - wan2.2-i2v-flash: Recommended 262144. Range: 65536–262144. - wan2.2-kf2v-flash: Recommended 262144. Range: 65536–262144.	Varies by model
split	float	No	Training set split ratio. The proportion of the dataset used for training, with a valid range of (0, 1). This parameter is ignored if validation_file_ids is specified.	0.9
max_split_val_dataset_sample	int	No	Maximum samples for auto-split validation set. The size of the validation set is the smaller of two values: the result of total_samples × (1 − split) or the value of this parameter.	5
save_total_limit	int	No	Checkpoint save limit. The maximum number of recent checkpoints to keep. The system deletes older checkpoints once this limit is exceeded.	10
lora_rank	int	No	LoRA rank. The rank (dimension) of the LoRA low-rank matrices. Must be a power of 2 (2n), such as 16, 32, or 64.	32
lora_alpha	int	No	LoRA alpha. The scaling factor for the LoRA weights. Must be a power of 2 (2n), such as 16, 32, or 64.	32

Example response

Text generation model

Video and image generation model

{
          "request_id": "9654e55a-d74b-4113-aee1-fa19c9384fcc",
          "output": {
              "job_id": "ft-202410291653-1c7f",
              "job_name": "ft-202410291653-1c7f",
              "status": "PENDING",
              "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",
              "group": "llm"
          }
      }

Focus on output.job_id (job ID) and output.finetuned_output (the name of the new model generated after fine-tuning, which is used for deployment).

Video generation model

{
    "request_id": "0eb05b0c-02ba-414a-9d0c-xxxxxxxxx",
    "output": {
        "job_id": "ft-202511111122-xxxx",
        "job_name": "ft-202511111122-xxxx",
        "status": "PENDING",
        "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,
            "batch_size": 2,
            "learning_rate": 2.0E-5,
            "split": 0.9,
            "eval_epochs": 50
        },
        "training_type": "efficient_sft",
        "create_time": "2025-11-11 11:22:22"
    }
}

Response parameters

Parameter	Type	Description

Parameter	Type	Description
request_id	String	The ID of the request.
output	Object	Details of the fine-tuning job.
output.job_id	String	The ID of the fine-tuning job. You can use this ID with other APIs, such as querying fine-tuning job details, querying fine-tuning job logs, canceling a fine-tuning job, and deleting a fine-tuning job. Format: ft-{yyyyMMddHHmm}-{4-character ID}.
output.jobs_name	String	Same as output.job_id.
output.status	String	The job status.
output.model	String	The ID of the model that was fine-tuned.
output.base_model	String	The ID of the base model used for fine-tuning. Example: For the fine-tuning job ft-202410291653-1c7f, the base model is qwen3-14b.
output.training_file_ids	Array	An array of fine-tuning file IDs.
output.validation_file_ids	Array	An array of validation file IDs.
output.hyper_parameters	Object	The hyperparameters explicitly set for the job.
output.training_type	String	The fine-tuning method.
output.create_time	String	The time the fine-tuning job was created.
output.workspace_id	String	The ID of the workspace that contains the fine-tuning job.
output.user_identity	String	The UID of the owning main account.
output.modifier	String	The UID of the account that last modified the job. For example, if a sub-account cancels the job, this field returns the UID of that sub-account.
output.creator	String	The UID of the user who created the job.
output.group	String	The job type for model fine-tuning.

Job status	Description

Job status	Description
PENDING	The fine-tuning job is waiting to start.
QUEUING	The fine-tuning job is in the queue. (Only one fine-tuning job can run at a time.)
RUNNING	The fine-tuning job is running.
CANCELING	The fine-tuning job is being canceled.
SUCCEEDED	The fine-tuning job has succeeded.
FAILED	The fine-tuning job has failed.
CANCELED	The fine-tuning job has been canceled.

Request error codes

Returned when a request fails.

Parameter	Type	Description	Example

Parameter	Type	Description	Example
code	String	The error code.	NotFound
request_id	String	The system-generated unique ID for this request.	6332fb02-3111-43f0-bf79-f9e8c5ffa7f9
message	String	The error message.	Not Found!

Example response

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

Error codes

HTTP status code	Error code	Example	Description	Solution

HTTP status code	Error code	Example	Description	Solution
400	InvalidParameter	Missing training files	A parameter is invalid, either because a required parameter is missing or a value has an incorrect format.	Check the error message and correct the parameters in your request.
400	UnsupportedOperation	The fine-tune job cannot be deleted because it has already succeeded, failed, or been canceled.	The resource is in a state that prevents this operation.	Retry the operation after the resource enters an operational state.
404	NotFound	Not found!	The requested resource does not exist.	Verify that the resource ID is correct.
409	Conflict	Model instance xxxxx already exists, please specify a suffix	A deployment instance with the specified name already exists.	Specify a unique suffix for the deployment.
429	Throttling	- Too many fine-tune jobs are running. Please retry later. - Each user is allowed a maximum of 20 fine-tune jobs that are running or have succeeded.	The request was rejected because a platform limit was reached.	- Delete unused models.
500	InternalError	Internal server error!	An internal error occurred.	Record the request_id and submit a ticket to Alibaba Cloud support for troubleshooting.

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Create a fine-tuning job

Input parameters

Example response

Response parameters

Request error codes

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