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

# Controlling the Level of Fine-Tuning

> Learn how to use the finetune_depth parameter to control the extent of fine-tuning in TimeGPT models.

## Controlling the Level of Fine-Tuning

It is possible to control the depth of fine-tuning with the `finetune_depth`
parameter.

`finetune_depth` takes values among `[1, 2, 3, 4, 5]`. By default, it is set to
1, which means that a small set of the model's parameters are being adjusted,
whereas a value of 5 fine-tunes the maximum amount of parameters.

Increasing `finetune_depth` also increases the time to generate predictions.
While it can generate better results, we must be careful to not overfit the
model, in which case the predictions may not be as accurate.

Let's run a small experiment to see how `finetune_depth` impacts the performance.

## How to Control the Level of Fine-Tuning

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/tutorials/23_finetune_depth_finetuning.ipynb)

### Step 1: Import Packages

First, we import the required packages and initialize the Nixtla client

```python theme={null}
import pandas as pd
from nixtla import NixtlaClient
from utilsforecast.losses import mae, mse
from utilsforecast.evaluation import evaluate
```

```python theme={null}
nixtla_client = NixtlaClient(
    # defaults to os.environ.get("NIXTLA_API_KEY")
    api_key='my_api_key_provided_by_nixtla'
)
```

### Step 2: Load Data

Next, load the dataset

```python theme={null}
df = pd.read_csv(
    'https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv'
)
df.head()
```

Now, we split the data into a training and test set so that we can measure the
performance of the model as we vary `finetune_depth`.

```python theme={null}
train = df[:-24]
test = df[-24:]
```

### Step 3: Fine-Tuning With finetune\_depth

As mentioned above, `finetune_depth` controls how many parameters from TimeGPT
are fine-tuned on your particular dataset. If the value is set to 1, only a few
parameters are fine-tuned. Setting it to 5 means that all parameters of the
model will be fine-tuned.

Using a large value for `finetune_depth` can lead to better performances for
large datasets with complex patterns. However, it can also lead to overfitting,
in which case the accuracy of the forecasts may degrade, as we will see from the
small experiment below.

```python theme={null}
depths = [1, 2, 3, 4, 5]

test = test.copy()

for depth in depths:
    preds_df = nixtla_client.forecast(
        df=train,
        h=24,
        finetune_steps=5,
        finetune_depth=depth,
        time_col='timestamp',
        target_col='value'
    )

    preds = preds_df['TimeGPT'].values
    test.loc[:, f'TimeGPT_depth{depth}'] = preds
```

Evaluate the forecasts using MAE and MSE metrics:

```python theme={null}
test['unique_id'] = 0

evaluation = evaluate(
    test,
    metrics=[mae, mse],
    time_col="timestamp",
    target_col="value"
)
evaluation
```

| unique\_id | metric | TimeGPT\_depth1 | TimeGPT\_depth2 | TimeGPT\_depth3 | TimeGPT\_depth4 | TimeGPT\_depth5 |
| ---------- | ------ | --------------- | --------------- | --------------- | --------------- | --------------- |
| 0          | mae    | 22.675540       | 17.908963       | 21.318518       | 24.745096       | 28.734302       |
| 0          | mse    | 677.254283      | 461.320852      | 676.202126      | 991.835359      | 1119.722602     |

From the result above, we can see that a `finetune_depth` of 2 achieves the best
results since it has the lowest MAE and MSE.

Also notice that with a `finetune_depth` of 4 and 5, the performance degrades,
which is a clear sign of overfitting.

Thus, keep in mind that fine-tuning can be a bit of trial and error. You might
need to adjust the number of `finetune_steps` and the level of `finetune_depth`
based on your specific needs and the complexity of your data. Usually, a higher
`finetune_depth` works better for large datasets. In this specific tutorial,
since we were forecasting a single series with a very short dataset, increasing
the depth led to overfitting.

It's recommended to monitor the model's performance during fine-tuning and
adjust as needed. Be aware that more `finetune_steps` and a larger value of
`finetune_depth` may lead to longer training times and could potentially lead
to overfitting if not managed properly.