Fine-tune DeepSeek-R1 distill models | PAI Model Gallery - Platform For AI

Supported models

PAI Model Gallery supports LoRA supervised fine-tuning (SFT) for six distill models. The following table lists the recommended minimum resource configurations when using default hyperparameters and the provided dataset.

Distill Model	Base Model	Supported Training Method	Minimum Configuration
DeepSeek-R1-Distill-Qwen-1.5B	Qwen2.5-Math-1.5B	LoRA supervised fine-tuning	1 × A10 (24 GB video memory)
DeepSeek-R1-Distill-Qwen-7B	Qwen2.5-Math-7B		1 × A10 (24 GB video memory)
DeepSeek-R1-Distill-Llama-8B	Llama-3.1-8B		1 × A10 (24 GB video memory)
DeepSeek-R1-Distill-Qwen-14B	Qwen2.5-14B		1 × GU8IS (48 GB video memory)
DeepSeek-R1-Distill-Qwen-32B	Qwen2.5-32B		2 × GU8IS (48 GB video memory)
DeepSeek-R1-Distill-Llama-70B	Llama-3.3-70B-Instruct		8 × GU100 (80 GB video memory)

Quick start

Go to the Model Gallery page.
1. Log on to the PAI console. In the left-side navigation pane, select your target Workspace.
2. In the left-side navigation pane, choose QuickStart > Model Gallery.
On the Model Gallery page, search for and click DeepSeek-R1-Distill-Qwen-7B. The model details page describes training and deployment, including the required SFT data format and invocation methods.

Click Train in the upper-right corner. Configure the following key parameters:

Dataset configuration: This example uses the default dataset. To use your own data, prepare a custom dataset per the format on the model details page and upload it to an Object Storage Service (OSS) bucket.
Model output path: Select an OSS path to store the fine-tuned model.
Computing Resources: For Source, select public resource. For Instance type, select ecs.gn7i-c16g1.4xlarge.

Hyperparameters: Supported hyperparameters for LoRA SFT. Adjust as needed. Fine-tuning guide for large language models.

Hyperparameters

Parameter	Type	Default (for 7B model)	Description
learning_rate	float	5e-6	Controls the magnitude of model weight updates.
num_train_epochs	int	6	Number of passes over the training dataset.
per_device_train_batch_size	int	2	Samples processed per GPU per iteration. Larger values improve throughput but require more GPU memory.
gradient_accumulation_steps	int	2	The number of gradient accumulation steps.
max_length	int	1024	Maximum input token length per training iteration.
lora_rank	int	8	The LoRA dimension.
lora_alpha	int	16	The LoRA scaling factor.
lora_dropout	float	0	Dropout rate for LoRA training. Prevents overfitting by randomly dropping neurons.
lorap_lr_ratio	float	16	The LoRA+ learning rate ratio (λ = ηB/ηA), where ηA and ηB are the learning rates for adapter matrices A and B. LoRA+ applies different learning rates to matrices A and B for better performance and faster convergence without extra compute. Set `lorap_lr_ratio` to 0 to use standard LoRA.

Click Train. PAI redirects you to the training job page where you can monitor job status and view logs.

When training succeeds, the fine-tuned model is automatically registered in AI Asset Management - Models, where you can view or deploy it. Register and manage models.
After training completes, click Deploy to deploy the fine-tuned model as an EAS service. Use the same invocation method as the original distill model, described on the model details page and in Deploy DeepSeek-V3 and DeepSeek-R1 models.

Billing

Model Gallery training uses DLC, billed by job duration. Billing for DLC.

FAQ

Q: How do I troubleshoot a failed training job?

Set an appropriate max_length in the training configuration. The algorithm discards data exceeding max_length and logs the action:

If too much data is discarded, the training or validation dataset might become empty, causing the training job to fail:
The error failed to compose dlc job specs, resource limiting triggered, you are trying to use more GPU resources than the threshold means the job hit a GPU resource limit. By default, a maximum of 2 GPUs can run simultaneously. Wait for running jobs to complete, or submit a ticket to request a quota increase.
The error the specified vswitch vsw-**** cannot create the required resource ecs.gn7i-c32g1.8xlarge, zone not match means the instance type is unavailable in the vSwitch's availability zone. Solutions: 1. Remove the vSwitch setting so DLC auto-selects an available zone. 2. Switch to a different instance type.

Q: Can I download the model after training?

Yes. Set the model output path to an OSS directory when creating the training job. After the job completes, download the model from that OSS path.

Q: What should I do if the model performance is poor?

Try the following:

Use a model with better baseline performance, such as a model from the DeepSeek or Qwen3 series with a higher parameter count.
Refine your prompts.
Increase the max_tokens value.
Break complex tasks into smaller subtasks.

Platform For AI:Fine-tune DeepSeek-R1 distill models