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Access to an all-inclusive stack for AI development, driven by NVIDIA’s powerful technology!
Enhance application capacity and availability.
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Fine-tune with LoRA
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
- Go to the Fine-tuning Jobs page and click + Fine-tune model.
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In the pop-up, enter the Name of your fine-tuning job.
- Validation: Required, max 100 characters, supports Unicode letters, digits,
-,_,.
- Validation: Required, max 100 characters, supports Unicode letters, digits,
- Select a Base model from the dropdown list.
- Examples:
gemma-3-27b-it,Qwen3-4B-Instruct-2507,Llama-3.3-70B-Instruct
- Examples:
- Select dataset format from the dropdown list: Alpaca/ ShareGPT/ ShareGPT_Image
- Upload your Training data file.
- Supported formats: CSV, JSON, JSONL, ZIP, Parquet (<100MB).
- (Optional) Upload Validation data.
- (Optional) Configure hyperparameters:
- Learning rate: Float,
1e-6 → 1e-4(e.g.,0.00001) - Number of epochs: Integer
1–20(default =5)
- Learning rate: Float,
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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.
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)
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