⚙️Fine-tune with LoRA

How to create a Fine-tuning job with LoRA?

To fine-tune a model with LoRA, please follow the instructions below:

Notes

• You must log in before starting a fine-tune job.

• Ensure you have enough balance (credit).

• At least one base model must be available for fine-tuning.

Steps

1. Go to the Fine-tuning Jobs page and click + Fine-tune model.

2. In the pop-up, enter the Name of your fine-tuning job.

   • Validation: Required, max 100 characters, supports Unicode letters, digits, -, _, .

1. Select a Base model from the dropdown list.

   • Examples: gemma-3-27b-it, Qwen3-4B-Instruct-2507, Llama-3.3-70B-Instruct

2. Select dataset format from the dropdown list: Alpaca/ ShareGPT/ ShareGPT_Image

3. Upload your Training data file.

   • Supported formats: CSV, JSON, JSONL, ZIP, Parquet (<100MB).

4. (Optional) Upload Validation data.

5. (Optional) Configure hyperparameters:

   • Learning rate: Float, 1e-6 → 1e-4 (e.g., 0.00001)

   • Number of epochs: Integer 1–20 (default = 5)

6. Click Create to start the fine-tuning job.

   • The job will appear in the table with status Running.

   Note: Fine-tuning with LoRA usually takes only a few minutes.

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How to manage Fine-tuning jobs?

On the Fine-tuning Jobs page, you can:

• View detail: Open the pipeline detail in AI Studio.

• Deploy model: Once training is completed, deploy the LoRA model.

• Cancel job: Cancel a running job (requires confirmation).

• Delete job: Permanently delete a job (requires confirmation).

Status badges

• Running (yellow)

• Succeeded (green)

• Failed (red)

• Canceled (gray)