# Querying the Models

After the launch, query the model using either the `/generate` or `/generate_stream` routes:

```shell
curl 127.0.0.1:8080/generate \
    -X POST \
    -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
    -H 'Content-Type: application/json'
```

```shell
curl 127.0.0.1:8080/generate_stream \
    -X POST \
    -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
    -H 'Content-Type: application/json'
```

or through Python:

```shell
pip install text-generation
```

Then run:

```python
from text_generation import Client

client = Client("http://127.0.0.1:8080")
print(client.generate("What is Deep Learning?", max_new_tokens=20).generated_text)

text = ""
for response in client.generate_stream("What is Deep Learning?", max_new_tokens=20):
    if not response.token.special:
        text += response.token.text
print(text)
```

## API documentation
You can consult the OpenAPI documentation of the `text-generation-inference` REST API using the `/docs` route. The Swagger UI is also available [here](https://huggingface.github.io/text-generation-inference).