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

# Train Module

> Training and fine-tuning capabilities for PraisonAI

# Train Module

The Train module provides training and fine-tuning capabilities for PraisonAI agents and models.

## Import

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonai.train import Trainer
```

## Quick Example

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonai.train import Trainer

# Create trainer
trainer = Trainer(
    model="gpt-4o-mini",
    training_data="training_data.jsonl"
)

# Start training
result = trainer.train()
print(f"Fine-tuned model: {result.model_id}")
```

## Features

* Fine-tune models on custom datasets
* Support for JSONL training data format
* Integration with OpenAI fine-tuning API
* Training progress monitoring
* Model validation and evaluation

## Training Data Format

Training data should be in JSONL format:

```jsonl theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
{"messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}]}
{"messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What is AI?"}, {"role": "assistant", "content": "AI stands for Artificial Intelligence..."}]}
```

## Constructor

### `Trainer(model, training_data)`

Creates a new Trainer instance.

**Parameters:**

| Parameter         | Type  | Default  | Description                |
| ----------------- | ----- | -------- | -------------------------- |
| `model`           | `str` | Required | Base model to fine-tune    |
| `training_data`   | `str` | Required | Path to training data file |
| `validation_data` | `str` | `None`   | Path to validation data    |
| `epochs`          | `int` | `3`      | Number of training epochs  |
| `batch_size`      | `int` | `4`      | Training batch size        |

## Methods

### `train()`

Start the training process.

**Returns:** `TrainingResult` - Contains model\_id and metrics

### `validate()`

Validate the training data format.

**Returns:** `bool` - True if valid

### `get_status(job_id)`

Get the status of a training job.

**Parameters:**

* `job_id` (str): The training job ID

**Returns:** `dict` - Job status and progress

### `cancel(job_id)`

Cancel a running training job.

**Parameters:**

* `job_id` (str): The training job ID

## Example: Full Training Workflow

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonai.train import Trainer

# Prepare training data
training_data = [
    {
        "messages": [
            {"role": "system", "content": "You are a customer support agent."},
            {"role": "user", "content": "How do I reset my password?"},
            {"role": "assistant", "content": "To reset your password..."}
        ]
    }
]

# Save as JSONL
import json
with open("training.jsonl", "w") as f:
    for item in training_data:
        f.write(json.dumps(item) + "\n")

# Create trainer
trainer = Trainer(
    model="gpt-4o-mini",
    training_data="training.jsonl",
    epochs=3
)

# Validate data
if trainer.validate():
    # Start training
    result = trainer.train()
    
    # Monitor progress
    while True:
        status = trainer.get_status(result.job_id)
        print(f"Status: {status['status']}")
        if status['status'] in ['succeeded', 'failed']:
            break
    
    # Use fine-tuned model
    print(f"Fine-tuned model: {result.model_id}")
```

## Related

* [Agent Module](/docs/sdk/praisonaiagents/agent/agent) - Use trained models with agents
* [LLM Module](/docs/sdk/praisonaiagents/llm/llm) - LLM configuration