Deploy and train models from PAI Model Gallery - Platform For AI

Select a model

When selecting a model, consider the following factors:

Search by domain and task: Filter models by application domain and task type.
Check the pretraining dataset: A pretraining dataset that closely matches your use case yields better deployment and fine-tuning performance. View dataset details on the model details page.
Consider the model size: Larger models generally perform better but cost more to serve and require more data for fine-tuning.

To find a model:

Go to the Model Gallery page.
1. Log on to the PAI console.
2. In the left-side navigation pane, click Workspaces, and then select a workspace.
3. In the left-side navigation pane, click QuickStart > Model Gallery to go to the Model Gallery page.
Find a model.

After you find a model, deploy it, debug it online, or verify its inference performance. Deploy a model, Fine-tune a model.

Deploy a model

To deploy Qwen3-0.6B as an example, follow Model Gallery Quick Start - Model Deployment.

Fine-tune a model

To fine-tune Qwen3-0.6B as an example, follow Model Gallery Quick Start - Model Fine-tuning.

Configure the following parameters on the fine-tuning job details page.

Configurable parameters for fine-tuning

Parameter type	Parameter	Description
Training Mode	Supervised Fine-tuning (SFT)	Supported training modes: Supervised Fine-tuning (SFT): Fine-tunes model parameters with specified input-output pairs. Direct Preference Optimization (DPO): Aligns a language model with human preferences, sharing the same goal as Reinforcement Learning from Human Feedback (RLHF). Both modes support full-parameter fine-tuning, LoRA, and QLoRA.
Training Mode	Direct Preference Optimization (DPO)
Job Configuration	Task name	A default name is provided. Modify as needed.
Job Configuration	Maximum running time	Maximum task duration. The task stops when exceeded. Default: no time limit.
Dataset Configuration	Training dataset	Default training data is provided. To use a custom dataset, prepare it in the format specified in the model documentation and upload it by using one of the following methods: OSS file or directory Click to select the OSS path of your dataset. In the Select OSS folder or file dialog box, select an existing file or click Upload file. Custom Dataset Use datasets stored in cloud storage such as OSS. Click to select an existing dataset. If no dataset exists, create one by following Create and manage datasets.
Dataset Configuration	Validate dataset	Click Add validation dataset to add one. Configure it the same way as Training dataset.
Output Configuration		Cloud storage path for the trained model and TensorBoard log files. Note If a default OSS path is configured on the workspace details page, this field is auto-populated. Manage workspaces.
Computing Resources	Resource Type	Supported: General Computing and Lingjun Intelligent Computing.
Computing Resources	Source	Public Resources: Billing mode: pay-as-you-go. Use cases: Small-scale tasks without strict latency requirements. Resource Quota: General Computing or Lingjun Intelligent Computing resources. Billing mode: subscription. Use cases: Larger workloads requiring high availability and guaranteed execution. Preemptible Resources: Billing mode: pay-as-you-go. Use cases: Offered at a significant discount to reduce costs. Limitations: Availability is not guaranteed. Resources may be unavailable or reclaimed. Use preemptible jobs.
Hyperparameters		Hyperparameters vary by model. Use defaults or modify as needed.

Note

Available parameters vary by model. Adjust based on your model's requirements.

Billing

Model Gallery is free. You are charged for EAS and DLC resources consumed during deployment and training. Billing for Elastic Algorithm Service (EAS), Billing for Deep Learning Containers (DLC).

References

Model Gallery FAQ