> ## Documentation Index
> Fetch the complete documentation index at: https://docs.slai.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Using Pre-Trained Models
> Here's a quick guide on how to use pre-trained models in Slai.
You may wish to use Slai for packaging and deployment, while doing your training elsewhere. Let's walk through a quick tutorial on how you can use pre-trained models in your Slai project.
In this example, we'll use a pre-trained GPT-2 model from HuggingFace.
**Let's start with `train.py`**. Simply import your pre-trained model in `train.py` and return the model class in `train()`
```python train.py theme={null}
import transformers import pipeline, set_seet
def train():
mode = pipeline("text-generation", model="gpt-2")
set_seed(42)
return model
```
**Now, let's move onto `handler.py`**. In your handler, you can define how you'd like to handle the inputs and outputs to the pre-trained model. In this example, we'll pass a string input, and return a JSON object with generated sentences from GPT-2.
```python handler.py theme={null}
import slai
class ModelHandler(slai.BaseModelHander):
inputs = {"text": slai.types.String()}
outputs = {
"prediction": slai.types.Json(),
}
def call(self, artifact, **inputs):
gpt2 = artifact
prediction = gpt2(inputs["text"], max_length=500, num_return_sequences=5)
return {"prediction": prediction}
```
Even though the model is trained, you'll need to click `Train` to download the pre-trained weights into the sandbox. Once that is complete, you can test the model before deploying.
In the test panel, you'll get an interactive test utility to send requests to the model. Let's generate some sentences!
