> ## Documentation Index
> Fetch the complete documentation index at: https://docs.seekr.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Low-rank adaptation

> Apply low-rank adaptation to fine-tuning jobs with the SeekrFlow Python SDK.

For conceptual background on LoRA, including when to use it and how it compares to full fine-tuning, see [Low-rank adaptation (LoRA)](/flow/components/fine-tuning/lora).

## Add LoRA to a fine-tuning job

To use LoRA, set `lora_config` in your `TrainingConfig` using `LoRAConfig`. LoRA works with all fine-tuning methods: instruction, reinforcement, preference, and vision language tuning.

<CodeGroup>
  ```python Python expandable theme={null}
  from seekrai import SeekrFlow
  from seekrai.types import TrainingConfig, InfrastructureConfig
  from seekrai.types.finetune import LoRAConfig

  client = SeekrFlow()

  training_config = TrainingConfig(
      training_files=["<your-training-file-id>"],
      model="meta-llama/Meta-Llama-3-8B-Instruct",
      n_epochs=1,
      n_checkpoints=1,
      batch_size=4,
      learning_rate=1e-5,
      experiment_name="helperbot_lora_v1",
      lora_config=LoRAConfig(
          r=16,
          alpha=32,
      ),
  )

  infrastructure_config = InfrastructureConfig(
      n_accel=8,
      accel_type="GAUDI2",
  )

  fine_tune = client.fine_tuning.create(
      training_config=training_config,
      infrastructure_config=infrastructure_config,
      project_id=123,
  )

  print(fine_tune.id)
  ```
</CodeGroup>

## LoRAConfig parameters

| Parameter | Type    | Default  | Description                                                                                              |
| --------- | ------- | -------- | -------------------------------------------------------------------------------------------------------- |
| `r`       | integer | `8`      | Adapter rank. Controls adapter capacity and the complexity of patterns the adapter can learn.            |
| `alpha`   | float   | `32`     | Scaling factor for adapter influence. Controls how strongly learned patterns affect base model behavior. |
| `dropout` | float   | `0.1`    | Dropout probability applied to adapter layers during training.                                           |
| `bias`    | string  | `"none"` | Whether to train bias parameters. Accepted values: `"none"`, `"all"`, `"lora_only"`.                     |
| `extras`  | object  | —        | Optional dictionary for additional LoRA parameters.                                                      |

For guidance on choosing rank and alpha values, see [Low-rank adaptation (LoRA)](/flow/components/fine-tuning/lora).

See [Create a fine-tuning job](/flow/sdk/fine-tuning/create-fine-tuning-job) for the full workflow including project setup, file retrieval, and monitoring.