# LoRA Training Guide 🧠

## Overview

This guide will help you understand and use LoRA (Low-Rank Adaptation) training with our models. LoRA allows you to fine-tune models for specific styles or subjects while maintaining efficiency.

## Available Models

- [flux-lora-trainer](https://app.flymy.ai/flymyai/flux-lora-trainer) - Train custom LoRA models

## Basic Usage

Here's a simple example of how to train a LoRA model:

```python
from flymyai import client

# Initialize the client
fma_client = client(apikey="your-api-key")

# Set the model
model = "flymyai/flux-lora-trainer"

# Prepare the input data
payload = {
    "lora_name": "Hello World",
    "trigger_word": "Hello World",
    "dataset_url": "https://example.com/dataset.zip",
    "training_steps": 1000,
    "huggingface_model_name": "black-forest-labs/FLUX.1-dev"
}

# Make the prediction
response = fma_client.predict(
    model=model,
    payload=payload
)

# Get the trained model and config
trained_model = response.output_data["model"]
config = response.output_data["config"]
```

## Parameters

- `lora_name`: Name for your LoRA model
- `trigger_word`: Word to activate the LoRA model
- `dataset_url`: URL to your training dataset
- `training_steps`: Number of training steps
- `huggingface_model_name`: Base model to use for training

## Best Practices

1. Use high-quality training images
2. Choose appropriate training steps
3. Use meaningful trigger words
4. Organize your dataset properly
5. Test the model after training

## Related Guides

- [Image Generation](https://docs.flymy.ai/guides/generate-images.md)
- [Video Generation](https://docs.flymy.ai/guides/generate-videos.md)
- [API Reference](https://docs.flymy.ai/api/overview.md)