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

# Fine-tune a Model

> End-to-end workflow: Dataset, Composition, Run, then Validation

This recipe walks through the complete lifecycle of creating a fine-tuned model in Maitai, from raw data to a deployed model.

## 1) Create a Dataset

The first step is gathering the data you want the model to learn from.

1. Go to **Forge > Finetuning**.
2. Click **New Dataset**.
3. Choose a source (e.g., **Production Traffic** to filter real requests, or **File Upload**).
4. Save the dataset.

See: [Dataset Creation](/build/models/dataset_creation)

## 2) Prepare & Refine Data

Once the dataset is created, you can improve its quality.

1. Open the Dataset.
2. Use **AI Review** to spot inconsistencies.
3. Edit individual requests to fix errors or improve the "ideal" response.
4. Apply **Modifications** (regex) or **Augmentations** (synthetic variations) if needed.

See: [Dataset Preparation](/build/models/dataset_preparation)

## 3) Create a Composition

A Composition is a "recipe" that combines one or more datasets. This lets you mix and match data (e.g., "80% production data + 20% golden test set") without duplicating the underlying requests.

1. Go to **Forge > Compositions**.
2. Click **New Composition**.
3. Select the datasets to include.
4. Adjust sampling weights if desired.

See: [Compositions](/build/models/compositions)

## 4) Start a Fine-tune Run

Now you're ready to train.

1. Go to **Forge > Finetuning** (or start directly from a Composition).
2. Click **New Finetune Run**.
3. Select your **Composition**.
4. Choose the **Base Model** (e.g., `llama-3.1-8b-instruct`).
5. Configure hyperparameters (epochs, learning rate) or stick to defaults.
6. Start the run.

See: [Fine-tune Runs](/build/models/fine_tune_runs)

## 5) Validate the Model

After training completes, Maitai automatically runs a validation step (if configured) or you can run one manually.

1. Open the **Fine-tune Run**.
2. Go to the **Validation** tab.
3. Review the **Validation Run** results to see how the new model performed against a holdout set or specific Test Set.
4. Check metrics like accuracy and pass rate.

See: [Validation](/build/models/validation)

## 6) Benchmarking against Golden Test Sets

Once you have a candidate model, the final hurdle before deployment is ensuring it actually outperforms your baseline on high-signal cases.

1. Go to **Test > Test Sets**.
2. Select your **Golden Test Set** for this intent.
3. Start a **New Test Run** using your newly fine-tuned model.
4. Use the **Compare Runs** feature to side-by-side the results of the new model against your current production model.
5. Verify that the new model maintains or improves performance on critical "golden" examples.

See: [Test Set Creation](/test/test_sets/creation), [Test Run Execution](/test/test_sets/execution)

## 7) Deploy

If the model performs well and passes your golden benchmarks:

1. Click **Deploy** on the Fine-tune Run page.
2. The model becomes available as a `ft:...` ID that you can use in your Applications or Intents configuration.

See: [Deployment](/build/models/deployment)