> ## 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.

# Text Generation

> Fine-tune a text generation model in minutes

## Overview

Fine-tune text generation models for chatbots, Q\&A systems, or content generation. This guide shows the minimal setup to get started.

## Your Data

Your training data should have two columns:

* **Input column** - The user prompt or question
* **Output column** - The expected response or answer

Example data in `train.parquet`:

| text                                | sentiment |
| ----------------------------------- | --------- |
| The product exceeded expectations   | positive  |
| Terrible customer service           | negative  |
| Average experience, nothing special | neutral   |

## Minimal Example

<CodeGroup>
  ```python Python theme={null}
  import requests

  url = "https://hub.oxen.ai/api/repos/YOUR_NAMESPACE/YOUR_REPO/fine_tunes"
  headers = {
      "Authorization": "Bearer YOUR_API_KEY",
      "Content-Type": "application/json"
  }

  # Create fine-tune
  data = {
      "resource": "main/train.parquet",
      "base_model": "meta-llama/Llama-3.2-1B-Instruct",
      "script_type": "text_generation",
      "training_params": {
          "question_column": "text",        # Your input column name
          "answer_column": "sentiment",      # Your output column name
          "epochs": 1
      }
  }

  response = requests.post(url, headers=headers, json=data)
  fine_tune_id = response.json()["fine_tune"]["id"]

  # Start training
  run_url = f"{url}/{fine_tune_id}/actions/run"
  requests.post(run_url, headers=headers)

  print(f"Fine-tune started: {fine_tune_id}")
  ```

  ```bash cURL theme={null}
  # Create fine-tune
  curl -X POST https://hub.oxen.ai/api/repos/YOUR_NAMESPACE/YOUR_REPO/fine_tunes \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "resource": "main/train.parquet",
      "base_model": "meta-llama/Llama-3.2-1B-Instruct",
      "script_type": "text_generation",
      "training_params": {
        "question_column": "text",
        "answer_column": "sentiment",
        "epochs": 1
      }
    }'

  # Start training (use the ID from the response)
  curl -X POST https://hub.oxen.ai/api/repos/YOUR_NAMESPACE/YOUR_REPO/fine_tunes/FINE_TUNE_ID/actions/run \
    -H "Authorization: Bearer YOUR_API_KEY"
  ```
</CodeGroup>

## Key Parameters

Only these fields are required to start:

| Parameter         | Description                             | Example                                 |
| ----------------- | --------------------------------------- | --------------------------------------- |
| `question_column` | Name of your input/prompt column        | `"text"`, `"question"`, `"prompt"`      |
| `answer_column`   | Name of your output/response column     | `"sentiment"`, `"answer"`, `"response"` |
| `epochs`          | Number of training passes (1-3 typical) | `1`                                     |

All other parameters use sensible defaults.

## Supported Models

Popular choices for text generation:

* `meta-llama/Llama-3.2-1B-Instruct` - Fast, good for Q\&A
* `meta-llama/Llama-3.2-3B-Instruct` - Balanced performance
* `meta-llama/Llama-3.1-8B-Instruct` - Higher quality, slower
* `Qwen/Qwen3-0.6B` - Very fast, lightweight

<Note>
  See the [full model list](/fine-tuning-api/reference/text_generation#supported-models) for all available options.
</Note>

## Monitor Progress

Check the status of your fine-tune:

```python theme={null}
status_url = f"https://hub.oxen.ai/api/repos/YOUR_NAMESPACE/YOUR_REPO/fine_tunes/{fine_tune_id}"
response = requests.get(status_url, headers=headers)
status = response.json()["fine_tune"]["status"]
print(f"Status: {status}")
```

Status values: `created`, `running`, `completed`, `errored`

## Next Steps

* [Advanced parameters](/fine-tuning-api/reference/text_generation) - Learning rate, batch size, LoRA configuration
* [Deploy your model](/getting-started/inference) - Use your fine-tuned model for inference
* [Full tutorial](/fine-tuning-api/tutorials/01_fine_tuning) - End-to-end walkthrough with monitoring

## Common Issues

<AccordionGroup>
  <Accordion title="Column not found error">
    Double-check your `question_column` and `answer_column` names match your data exactly. Column names are case-sensitive.
  </Accordion>

  <Accordion title="Out of memory error">
    Reduce `batch_size` to 1 or try a smaller model like `Llama-3.2-1B-Instruct`.
  </Accordion>

  <Accordion title="Training taking too long">
    Start with 1 epoch. If results aren't good enough, try 2-3 epochs. More isn't always better.
  </Accordion>
</AccordionGroup>