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

# Fine-Tuning Overview

> Learn how to fine-tune models on Oxen.ai to customize them for your specific use cases

## What is Fine-Tuning?

Fine-tuning allows you to customize pre-trained models with your own data, adapting them to your specific use cases. On Oxen.ai, you can fine-tune models for:

* **Text Generation** - Chatbots, Q\&A systems, content generation
* **Image Generation** - Custom image styles, branded content
* **Image Editing** - Style transfer, image-to-image transformations
* **Video Generation** - Custom video styles and content
* **Vision-Language Tasks** - Image captioning, visual Q\&A

## Getting Started

### Prerequisites

Before fine-tuning, you need:

1. **An Oxen.ai account** - [Sign up](https://oxen.ai/register) if you haven't already
2. **A repository** with your training data
3. **Training data** in a supported format (Parquet, CSV, etc.)
4. **An API key** for authentication

### Authentication

All fine-tuning API requests require authentication using a bearer token:

```bash theme={null}
curl -H "Authorization: Bearer YOUR_API_KEY" \
  https://hub.oxen.ai/api/repos/{namespace}/{repo}/fine_tunes
```

Get your API key from your [account settings](https://oxen.ai/settings/profile).

## Request Structure

All fine-tuning requests follow the same base structure:

```json theme={null}
{
  "resource": "main/your-dataset.parquet",
  "base_model": "<model-canonical-name>",
  "script_type": "<operation-type>",
  "training_params": {
    // Operation-specific parameters
  }
}
```

### Common Fields

| Field             | Description                                                       | Required |
| ----------------- | ----------------------------------------------------------------- | -------- |
| `resource`        | Path to your training data (e.g., `main/train.parquet`)           | Yes      |
| `base_model`      | The model to fine-tune (e.g., `meta-llama/Llama-3.2-1B-Instruct`) | Yes      |
| `script_type`     | The type of fine-tuning operation                                 | Yes      |
| `training_params` | Operation-specific training parameters                            | Yes      |

### Operation Types (`script_type`)

The `script_type` determines what kind of fine-tuning you're doing:

* `text_generation` - For text-based models (Q\&A, chatbots, completion)
* `text_chat_messages` - For conversational chat models
* `image_generation` - For text-to-image models
* `image_editing` - For image-to-image transformation
* `image_to_text` - For image captioning and VLMs
* `image_to_video` - For image-to-video generation
* `text_to_video` - For text-to-video generation
* `multi_image_editing` - For multi-image editing models

## Common Training Parameters

While each operation type has specific parameters, many share common training configuration:

### LoRA Parameters

Most fine-tuning uses LoRA (Low-Rank Adaptation) for efficient training:

* `use_lora` - Enable LoRA (typically `true`)
* `lora_rank` - Rank of LoRA matrices (default: 16, lower = faster/less memory)
* `lora_alpha` - LoRA scaling factor (default: 16)

### Training Configuration

* `batch_size` - Number of samples per training step (default: 1)
* `learning_rate` - Step size for optimization (typical: 0.0001-0.0002)
* `epochs` or `steps` - Training duration (text models use epochs, image models use steps)
* `gradient_accumulation` or `grad_accum` - Accumulate gradients across multiple steps

### Data Configuration

Each operation type requires specific data columns:

**Text models:**

* `question_column` - Input text column
* `answer_column` - Output/response column

**Image models:**

* `image_column` - Output image column
* `caption_column` - Text prompt column
* `control_image_column` - Input image column (for editing)

## Quick Start Guides

Choose your use case to get started with minimal examples:

<CardGroup cols={2}>
  <Card title="Text Generation" icon="message" href="/fine-tuning-api/quickstart-text">
    Fine-tune chatbots and Q\&A models
  </Card>

  <Card title="Image Generation" icon="image" href="/fine-tuning-api/quickstart-image-generation">
    Create custom image styles
  </Card>

  <Card title="Image Editing" icon="wand-magic-sparkles" href="/fine-tuning-api/quickstart-image-editing">
    Fine-tune image transformation models
  </Card>

  <Card title="Video Generation" icon="video" href="/fine-tuning-api/quickstart-video">
    Generate custom videos
  </Card>
</CardGroup>

## Detailed API Reference

For complete parameter documentation and advanced configuration:

* [Text Generation Reference](/fine-tuning-api/reference/text_generation)
* [Text Chat Messages Reference](/fine-tuning-api/reference/text_chat_messages)
* [Image Generation Reference](/fine-tuning-api/reference/image_generation)
* [Image Editing Reference](/fine-tuning-api/reference/image_editing)
* [Image to Text Reference](/fine-tuning-api/reference/image_to_text)
* [Image to Video Reference](/fine-tuning-api/reference/image_to_video)
* [Text to Video Reference](/fine-tuning-api/reference/text_to_video)
* [Multi-Image Editing Reference](/fine-tuning-api/reference/multi_image_editing)

## Parameter Guide

Learn about common training parameters and how to tune them:

* [Understanding LoRA](/fine-tuning-api/parameters#lora-low-rank-adaptation)
* [Learning Rate and Optimization](/fine-tuning-api/parameters#learning-rate-and-optimization)
* [Batch Size and Memory Management](/fine-tuning-api/parameters#batch-size-and-memory)
* [Training Duration](/fine-tuning-api/parameters#training-duration)

## Next Steps

1. **Choose your use case** from the Quick Start guides above
2. **Prepare your data** in the required format
3. **Start your first fine-tune** using the API
4. **Monitor progress** and deploy your model

Need help? Join our [Discord community](https://discord.com/invite/s3tBEn7Ptg) or check out the [detailed examples](/getting-started/fine-tuning).