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Free access Accelerate Delivery with Fixed-Cost Agentic CodingWatch how it works

Models ​

Empowering AI innovation for both enterprises and developers with Alibaba Cloud’s best-in-class Qwen models, AI-native apps, and AI solutions.

Alibaba Cloud Model Studio \ Enterprise-grade large model service and application development platform.

Try Visual Model \ Supports image understanding, image generation, and video generation.

Models ​

HappyHorse-1.0-T2V \ Cinematic creative generation, ultimate dynamic details Qwen3-VL-Plus \ Native VL, spatial reasoning, 1M-context video analysis Wan2.7-VideoEdit \ Supports both localized and global editing with prompt

Qwen3.6-Plus \ Native multimodal, 1M context, agentic coding Wan2.7-Image-Pro \ Interactive editing, long-text rendering, precise prompt following Qwen-Plus \ Balanced intelligence, efficient inference, production-ready performance

Qwen-Image-2.0 \ Professional infographics, exquisite photorealism Z-Image-Turbo \ Ultra-fast image generation, high throughput, cost-optimized inference Qwen3-Coder-Next \ Multi-turn tool interactions, future-ready development support

Wan2.7-T2V \ High-fidelity T2V, 15s duration, advanced camera control Wan2.7-I2V \ Cinematic I2V with emotional depth and visceral impact Wan2.7-R2V \ Up to 5 mixed image/video inputs and audio timbre cloning

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Qoder \ Intelligent coding assistant, available for enterprise-dedicated deployment. Qoder CN \ AI-powered coding assistant that boosts developer productivity with intelligent code completion, AI chat, multi-file editing, and task automation.

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Model Experience \ Experience full-scale, multimodal model capabilities online. Platform for AI \ An AI-native algorithm engineering platform for end-to-end modeling, training, and inference service deployment. Fine-tune Video Generation Model \ Customize Wan’s text-to-video capabilities through model fine-tuning to meet your unique requirements.

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AI Token Plan \ One plan. Multiple models. Big Savings with a Fixed Subscription. AI Image Creation \ All-in-one creative suite for copywriting, image generation, and poster design.

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Fine-tune Qwen in Alibaba Cloud Model Studio using an HTTP API.

Important

This topic is applicable only to the International Edition (Singapore region).

Prerequisites ​

  • You understand fine-tuning concepts, procedures, and data format requirements.

  • You have activated Model Studio and got an API key. See Create an API key.

Fine-tuning overview ​

Fine-tuning improves model performance:

  • Improve performance for specific industries or businesses

  • Reduce output latency

  • Suppress hallucinations

  • Align outputs with human values or preferences

  • Replace larger models with fine-tuned lightweight models

During fine-tuning, the model learns business- and scenario-specific features from your training data, such as domain knowledge, tone, communication style, and self-awareness. Because the model has already learned many industry- or scenario-specific examples during pre-training, its zero-shot or one-shot performance after fine-tuning surpasses the base model’s few-shot performance. This reduces input tokens and lowers output latency.

Overall procedure ​

Supported models ​

Text generation

NameModel codeFull-parameter SFT (sft)Efficient SFT (efficient_sft)
Qwen3-32Bqwen3-32bSupportedSupported
Qwen3-14Bqwen3-14bSupportedSupported
Qwen3-VL-8B-Instructqwen3-vl-8b-instructSupportedSupported
Qwen3-VL-8B-Thinkingqwen3-vl-8b-thinkingSupportedSupported

Compare training modes ​

Full-parameter trainingEfficient training (LoRA, recommended)
Scenarios• Learn new capabilities. • Achieve optimal overall performance.• Optimize performance in specific scenarios. • Time- and cost-sensitive.
Training durationLonger, with slower convergence.Shorter, with faster convergence.

Billing ​

MethodBilled by the volume of training data.
FormulaModel training fee = (Total tokens in training data + Total tokens in mixed training data) × Number of epochs × Training unit price (Minimum billing unit: 1 token)

Unit price for training

The following table lists the unit prices for training pre-trained models. The unit price for training a custom model matches that of the corresponding pre-trained model.

Qwen

Qwen-VL

ServiceCodePrice
Qwen3-32Bqwen3-32b$0.008/1,000 tokens
Qwen3-14Bqwen3-14b$0.0016/1,000 tokens
ServiceCodePrice
Qwen3-VL-8B-Instructqwen3-vl-8b-instruct$0.002/1,000 tokens
Qwen3-VL-8B-Thinkingqwen3-vl-8b-thinking$0.002/1,000 tokens

Dataset tips ​

Size requirements ​

SFT datasets require at least 1,000 high-quality entries. If evaluation results are unsatisfactory, collect more training data.

If you do not have enough data, consider building an agent application with a knowledge base. In many complex business scenarios, fine-tuning and knowledge base retrieval work best together.

For example, in a customer service scenario, fine-tune the model to adjust its tone, expression habits, and self-awareness, then use a knowledge base to dynamically inject domain knowledge into the context.

Try retrieval-augmented generation (RAG) first. After collecting enough data, use fine-tuning to further improve performance.

You can expand your dataset using the following strategies:

  1. Use a larger, high-performing model to generate content for specific businesses or scenarios.

  2. Manually collect data from various sources, such as application scenarios, web scraping, social media, forums, public datasets, partners, industry resources, and user contributions.

Data diversity and balance ​

For domain-specific use cases, domain expertise is the most important factor. For Q&A scenarios, generalization matters more. Design data samples based on your business modules or scenarios. Training quality depends on data volume, domain specificity, and diversity.

For example, in an AI assistant scenario, a professional and diverse dataset should include the following:

BusinessDiverse scenarios and use cases
BusinessDiverse scenarios and use cases
E-commerce customer servicePromotion pushes, pre-sales consultation, in-sales guidance, after-sales service, follow-up visits, complaint handling, and more.
Financial servicesLoan consultation, investment and financial advice, credit card services, bank account management, and more.
Online healthcareSymptom consultation, appointment scheduling, visit instructions, drug information queries, health tips, and more.
AI secretaryIT information, administrative information, HR information, employee benefit Q&A, company calendar queries, and more.
Travel assistantTravel planning, entry and exit guides, travel insurance consultation, destination customs and culture introductions, and more.
Corporate legal counselContract review, intellectual property protection, compliance checks, labor law Q&A, cross-border transaction consultation, case-specific legal analysis, and more.

Balance the data volume across scenarios to match actual usage ratios. This prevents bias toward any single feature type and improves generalization.

Upload a training dataset ​

Prepare a dataset ​

SFT training set

SFT for thinking model

SFT for image understanding (Qwen-VL)

SFT uses training data in Chat Markup Language (ChatML) format, which supports multi-turn conversations and multiple role settings.

The OpenAI name and weight parameters are not supported. All assistant outputs are trained.

json
# A line of training data (JSON format), the typical structure is as follows when expanded:
{"messages": [\
  {"role": "system", "content": "System input 1"},\
  {"role": "user", "content": "User input 1"},\
  {"role": "assistant", "content": "Expected model output 1"},\
  {"role": "user", "content": "User input 2"},\
  {"role": "assistant", "content": "Expected model output 2"}\
  ...\
]}

For details about the system, user, and assistant roles, see Overview of text generation models. Sample training sets: SFT-ChatML_format_example.jsonl and SFT-ChatML_format_example.xlsx. XLS and XLSX formats support only single-turn conversations.

For a single training entry, all assistant rows support the "loss_weight" parameter, which sets the relative importance during training. The valid values range from 0.0 to 1.0. Higher values indicate greater importance.

This parameter is in invitational preview. To use it, you can contact your account manager.

json
 {"role": "assistant", "content": "Expected model output 1", "loss_weight": 1.0},
 {"role": "assistant", "content": "Expected model output 2", "loss_weight": 0.5}

The training data supports multi-turn conversations and multiple role settings, but only the final assistant output is trained.

The \n characters before and after the think tags must be retained.

json
# A line of training data (JSON format), the typical structure is as follows when expanded:
{"messages": [\
  {"role": "system", "content": "System input 1"},\
  {"role": "user", "content": "User input 1"},\
  {"role": "assistant", "content": "Model output 1"}, --Intermediate assistant outputs should not have <think> tags\
   ...\
  {"role": "user", "content": "User input 2"},\
  {"role": "assistant", "content": "<think>\nExpected thinking content 2\n</think>\n\nExpected output 2"} --Thinking content can only be included in the final assistant output.\
]}

For details about the system, user, and assistant roles, see Overview of text generation models. Sample training set: SFT-deep_thinking_content_example.jsonl.

You can configure the model to omit the <think> tag in training samples. If you use this output method, do not enable the thinking mode for calls after the model is trained.

json
{"role": "assistant", "content": "Expected model output 2"}  --Tells the model not to enable thinking

The final assistant row of a single training entry supports the "loss_weight" parameter, which sets the relative importance during training. The valid values range from 0.0 to 1.0. Higher values indicate greater importance.

This parameter is in invitational preview. To use it, you can contact your account manager.

json
 {"role": "assistant", "content": "<think>\nExpected thinking content 2\n</think>\n\nExpected output 2", "loss_weight": 1.0}

The OpenAI name and weight parameters are not supported. All assistant outputs are trained.

For more information about the differences between the system, user, and assistant roles, see Overview of text generation models. Sample training data in ChatML format:

json
# A line of training data (JSON format), the typical structure is as follows when expanded:
{"messages":[\
  {"role":"user",\
    "content":[\
      {"text":"User input 1"},\
      {"image":"Image file name 1"}]},\
  {"role":"assistant",\
    "content":[\
      {"text":"Expected model output 1"}]},\
  {"role":"user",\
    "content":[\
      {"text":"User input 2"}]},\
  {"role":"assistant",\
    "content":[\
      {"text":"Expected model output 2"}]},\
  ...\
  ...\
  ...\
 ]}

Note

If you train a thinking model, you must follow the data format requirements for SFT for thinking model.

The following are the requirements for ZIP files:

  1. Format: ZIP. Maximum size: 2 GB. The folder and file names within the ZIP file must contain only ASCII letters (a–z, A–Z), numbers (0–9), underscores (_), and hyphens (-).

  2. The training text data file must be named data.jsonl and placed in the root directory of the ZIP file. Ensure that the data.jsonl file appears immediately when you open the ZIP file.

  3. A single image cannot exceed 1024 pixels in width or height. The maximum size is 10 MB. Supported formats: .bmp, .jpeg /.jpg, .png, .tif /.tiff, and .webp.

  4. Image file names cannot be duplicated, even if the files are stored in different folders.

  5. ZIP file directory structure:

Single-level directory (recommended)

Multi-level directory

The image files and the data.jsonl file reside in the root directory of the ZIP file.

markdown
Trainingdata_vl.zip
      |--- data.jsonl # Note: Do not wrap in an outer folder
      |--- image1.png
      |--- image2.jpg

  1. The data.jsonl file must be in the root directory of the ZIP file.

  2. In the data.jsonl file, you can declare only the image file name, not the file path. For example:

    Correct: image1.jpg. Incorrect: jpg_folder/image1.jpg.

  3. Image file names must be globally unique within the ZIP file.

markdown
Trainingdata_vl.zip
    |--- data.jsonl # Note: Do not wrap in an outer folder
    |--- jpg_folder
    |   └── image1.jpg
    |--- png_folder
        └── image2.png

Upload the training file ​

HTTP

For Windows CMD, you can replace ${DASHSCOPE_API_KEY} with %DASHSCOPE_API_KEY%. For PowerShell, you can replace it with $env:DASHSCOPE_API_KEY

shell
curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/files \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
--form 'file=@"path/to/your/sample.jsonl"' \
--form 'purpose="fine-tune"'

Note

Limits:

  • The maximum file size is 1 GB.

  • The total storage quota for all active (not deleted) files is 5 GB.

  • The maximum number of active (not deleted) files is 100.

  • There is no time limit for file storage.

See OpenAI compatible - File.

Response:

json
{
    "id": "file-ft-e73cafa11cef43a0ab75fb8e",
    "object": "file",
    "bytes": 23149,
    "filename": "qwen-fine-tune-sample.jsonl",
    "purpose": "fine-tune",
    "status": "processed",
    "created_at": 1769138847
}

Fine-tuning ​

Create a fine-tuning job ​

HTTP

For Windows CMD, you can replace ${DASHSCOPE_API_KEY} with %DASHSCOPE_API_KEY%. For PowerShell, you can replace it with $env:DASHSCOPE_API_KEY

curl
curl --location "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":[\
        "<Replace with the file ID of training dataset 1>",\
        "<Replace with the file ID of training dataset 2>"\
    ],
    "hyper_parameters":
    {
        "n_epochs": 1,
        "batch_size": 16,
        "learning_rate": "1.6e-5",
        "split": 0.9,
        "warmup_ratio": 0.0,
        "eval_steps": 1,
        "save_strategy": "epoch",
        "save_total_limit": 10
    },
    "training_type":"sft"
}'

Input parameters ​

FieldRequiredTypeLocationDescription
training_file_idsYesArrayBodyTraining set file IDs.
validation_file_idsNoArrayBodyValidation set file IDs.
modelYesStringBodyBase model ID, or the ID of a model generated by a previous fine-tuning job.
hyper_parametersNoMapBodyHyperparameters for fine-tuning. Default values are used if omitted.
training_typeNoStringBodyFine-tuning method. Valid values: sft efficient_sft
job_nameNoStringBodyJob name.
model_nameNoStringBodyName of the fine-tuned model. The model ID is generated by the system.

Sample response ​

json
{
    "request_id": "635f7047-003e-4be3-b1db-6f98e239f57b",
    "output":
    {
        "job_id": "ft-202511272033-8ae7",
        "job_name": "ft-202511272033-8ae7",
        "status": "PENDING",
        "finetuned_output": "qwen3-14b-ft-202511272033-8ae7",
        "model": "qwen3-14b",
        "base_model": "qwen3-14b",
        "training_file_ids":
        [\
            "9e9ffdfa-c3bf-436e-9613-6f053c66aa6e"\
        ],
        "validation_file_ids":
        [],
        "hyper_parameters":
        {
            "n_epochs": 1,
            "batch_size": 16,
            "learning_rate": "1.6e-5",
            "split": 0.9,
            "warmup_ratio": 0.0,
            "eval_steps": 1,
            "save_strategy": "epoch",
            "save_total_limit": 10
        },
        "training_type": "sft",
        "create_time": "2025-11-27 20:33:15",
        "workspace_id": "llm-8v53etv3hwb8orx1",
        "user_identity": "1654290265984853",
        "modifier": "1654290265984853",
        "creator": "1654290265984853",
        "group": "llm",
        "max_output_cnt": 10
    }
}

hyper_parameters supported settings ​

ParameterDefaultRecommended settingsTypeDescription
n_epochs1Adjust based on fine-tuning results.IntegerNumber 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.FloatControls the intensity of model weight updates. - Too high: parameters change drastically, degrading performance. - Too low: performance may not change significantly.
freeze_vittrueAdjust as needed.BooleanFreezes the visual backbone parameters so that its weights remain unchanged during training. Applies only to Qwen-VL models.
batch_sizeThe specific value varies depending on the selected model. The larger the model, the smaller the default batch size.Use the default value.IntegerNumber of data entries per training iteration. Smaller values prolong training time.
eval_steps50Adjust as needed.IntegerInterval (in steps) for evaluating training accuracy and loss during training. Controls display frequency of Validation Loss and Token Accuracy.
logging_steps5Adjust as needed.IntegerInterval (in steps) for printing fine-tuning logs.
lr_scheduler_typecosineRecommended: linear or Inverse_sqrtStringStrategy for dynamically adjusting the learning rate during training. Valid values:
max_length20488192IntegerMaximum token length per training entry. Entries exceeding this limit are discarded.
max_split_val_dataset_sample1000Use the default value.IntegerIf "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.
split0.8Use the default value.FloatIf 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_ratio0.05Use the default value.FloatProportion 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_decay0.1Use the default value.FloatL2 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_rank864IntegerRank of the low-rank matrix in LoRA. Higher ranks improve fine-tuning results but slightly slow down training.
lora_alpha32Use the default value.IntegerScaling 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_dropout0.1Use the default value.FloatDropout 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_strategyepochCan be set to epoch or steps. - When set to steps, set the save_steps parameter to adjust the saving interval.StringControls the interval and maximum number of checkpoints saved during fine-tuning.
save_steps50To modify it, set it to an integer multiple of the eval_steps parameter.IntegerNumber of training steps after which a checkpoint is saved.
save_total_limit110IntegerMaximum number of checkpoints to save for export.

Query job details ​

Use the returned job_id to query the job status.

HTTP

shell
curl 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<job_id>' \
--header 'Authorization: Bearer '${DASHSCOPE_API_KEY} \
--header 'Content-Type: application/json'

Input parameters ​

FieldTypeLocationRequiredDescription
job_idStringPath ParameterYesJob ID.

Sample successful response ​

json
{
    "request_id": "d100cddb-ac85-4c82-bd5c-9b5421c5e94d",
    "output":
    {
        "job_id": "ft-202511272033-8ae7",
        "job_name": "ft-202511272033-8ae7",
        "status": "RUNNING",
        "finetuned_output": "qwen3-14b-ft-202511272033-8ae7",
        "model": "qwen3-14b",
        "base_model": "qwen3-14b",
        "training_file_ids":
        [\
            "9e9ffdfa-c3bf-436e-9613-6f053c66aa6e"\
        ],
        "validation_file_ids":
        [],
        "hyper_parameters":
        {
            "n_epochs": 1,
            "batch_size": 16,
            "learning_rate": "1.6e-5",
            "split": 0.9,
            "warmup_ratio": 0.0,
            "eval_steps": 1,
            "save_strategy": "epoch",
            "save_total_limit": 10
        },
        "training_type": "sft",
        "create_time": "2025-11-27 20:33:15",
        "workspace_id": "llm-8v53etv3hwb8orx1",
        "user_identity": "1654290265984853",
        "modifier": "1654290265984853",
        "creator": "1654290265984853",
        "group": "llm",
        "max_output_cnt": 10
    }
}
Job statusMeaning
PENDINGTraining is about to begin.
QUEUINGJob is queued. Only one job can run at a time.
RUNNINGJob is running.
CANCELINGJob is being canceled.
SUCCEEDEDJob succeeded.
FAILEDJob failed.
CANCELEDJob was canceled.

Note

After training succeeds, finetuned_output contains the ID of the fine-tuned model, which you can use for model deployment.

Get job logs ​

HTTP

shell
curl 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<job_id>/logs?offset=0&line=1000' \
--header 'Authorization: Bearer '${DASHSCOPE_API_KEY} \
--header 'Content-Type: application/json'

Use the offset and line parameters to retrieve a specific range of logs. The offset parameter specifies the starting position and line specifies the maximum number of log entries.

Sample response:

json
{
    "request_id":"1100d073-4673-47df-aed8-c35b3108e968",
    "output":{
        "total":57,
        "logs":[\
            "{Log output 1}",\
            "{Log output 2}",\
            ...\
            ...\
            ...\
        ]
    }
}

Query and publish checkpoints ​

Only SFT fine-tuning (efficient_sft and sft) supports saving and publishing checkpoints of intermediate states.

Query checkpoints ​

shell
curl 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<job_id>/checkpoints' \
--header 'Authorization: Bearer '${DASHSCOPE_API_KEY} \
--header 'Content-Type: application/json'

Input parameters

FieldTypeLocationRequiredDescription
FieldTypeLocationRequiredDescription
job_idStringPath ParameterYesJob ID.

Sample successful response

json
{
    "request_id": "c11939b5-efa6-4639-97ae-ed4597984647",
    "output": [\
        {\
            "create_time": "2025-11-11T16:25:42",\
            "full_name": "ft-202511272033-8ae7-checkpoint-20",\
            "job_id": "ft-202511272033-8ae7",\
            "checkpoint": "checkpoint-20",\
            "model_name": "qwen3-14b-instruct-ft-202511272033-8ae7",\
            "status": "SUCCEEDED"\
        }\
    ]
}
Snapshot publishing statusDescription
Snapshot publishing statusDescription
PENDINGThe checkpoint is pending export.
PROCESSINGThe checkpoint is being exported.
SUCCEEDEDThe checkpoint was exported.
FAILEDThe checkpoint failed to export.

Note

The checkpoint parameter refers to the checkpoint ID, which is used to specify the checkpoint to export in the model publishing API. The model_name parameter refers to the model ID, which can be used for model deployment. The finetuned_output parameter returns the model_name of the last checkpoint.

Publish a model ​

Note

You can export a checkpoint after fine-tuning is complete. Export the checkpoint before deploying the model in Model Studio.

Exported checkpoints are stored in cloud storage and cannot be accessed or downloaded.

shell
curl --request GET 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<job_id>/export/<checkpoint_id>?model_name=<model_name>' \
--header 'Authorization: Bearer '${DASHSCOPE_API_KEY} \
--header 'Content-Type: application/json'

Input parameters

FieldTypeLocationRequiredDescription
FieldTypeLocationRequiredDescription
job_idStringPath ParameterYesJob ID.
checkpoint_idStringPath ParameterYesCheckpoint ID.
model_nameStringPath ParameterYesExpected model ID after export.

Sample successful response

json
{
    "request_id": "ed3faa41-6be3-4271-9b83-941b23680537",
    "output": true
}

Export is asynchronous. Monitor the export status by querying the checkpoint list.

Additional operations ​

List jobs ​

HTTP

HTTP

http
curl 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes' \
--header 'Authorization: Bearer '${DASHSCOPE_API_KEY} \
--header 'Content-Type: application/json'

Cancel a job ​

Terminate a running fine-tuning job.

HTTP

HTTP

http
curl --request POST 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<job_id>/cancel' \
--header 'Authorization: Bearer '${DASHSCOPE_API_KEY} \
--header 'Content-Type: application/json'

Delete a job ​

Running jobs cannot be deleted.

HTTP

HTTP

http
curl --request DELETE 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<job_id>' \
--header 'Authorization: Bearer '${DASHSCOPE_API_KEY} \
--header 'Content-Type: application/json'

Model deployment ​

Note

Fine-tuned models support only Model Unit deployment.

Go to the Model Deployment console (Singapore) to deploy a model. For more information about billing and other details, see Pay-as-you-go (model unit).

Call the model ​

After deploying a model, call it using OpenAI compatible APIs, Dashscope, or the Assistant SDK.

Set the model parameter to the model’s code. Go to the Model Deployment console (Singapore) to view the Model Code.

HTTP

HTTP

http
curl 'https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/text-generation/generation' \
--header 'Authorization: Bearer '${DASHSCOPE_API_KEY}  \
--header 'Content-Type: application/json' \
--data '{
    "model": "<Replace with the model instance Code after successful deployment>",
    "input":{
        "messages":[\
            {\
                "role": "user",\
                "content": "Who are you?"\
            }\
        ]
    },
    "parameters": {
        "result_format": "message"
    }
}'

FAQ ​

Can I upload and deploy my own models? ​

Uploading and deploying your own models is not currently supported. Follow the latest updates from Alibaba Cloud Model Studio.

However, Platform for AI (PAI) supports the deployment of your own models. See Deploy large language models in PAI-LLM.

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Prerequisites

Fine-tuning overview

Overall procedure

Supported models

Billing

Dataset tips

Size requirements

Data diversity and balance

Upload a training dataset

Prepare a dataset

Upload the training file

Fine-tuning

Create a fine-tuning job

Query job details

Get job logs

Query and publish checkpoints

Additional operations

Model deployment

Call the model

FAQ

Can I upload and deploy my own models?

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