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# Banana
>[Banana](https://www.banana.dev/) provided serverless GPU inference for AI models,
> a CI/CD build pipeline and a simple Python framework (`Potassium`) to server your models.
This page covers how to use the [Banana](https://www.banana.dev) ecosystem within LangChain.
## Installation and Setup
- Install the python package `banana-dev`:
```bash
pip install banana-dev
```
- Get an Banana api key from the [Banana.dev dashboard](https://app.banana.dev) and set it as an environment variable (`BANANA_API_KEY`)
- Get your model's key and url slug from the model's details page.
## Define your Banana Template
You'll need to set up a Github repo for your Banana app. You can get started in 5 minutes using [this guide](https://docs.banana.dev/banana-docs/).
Alternatively, for a ready-to-go LLM example, you can check out Banana's [CodeLlama-7B-Instruct-GPTQ](https://github.com/bananaml/demo-codellama-7b-instruct-gptq) GitHub repository. Just fork it and deploy it within Banana.
Other starter repos are available [here](https://github.com/orgs/bananaml/repositories?q=demo-&type=all&language=&sort=).
## Build the Banana app
To use Banana apps within Langchain, you must include the `outputs` key
in the returned json, and the value must be a string.
```python
# Return the results as a dictionary
result = {'outputs': result}
```
An example inference function would be:
```python
@app.handler("/")
def handler(context: dict, request: Request) -> Response:
"""Handle a request to generate code from a prompt."""
model = context.get("model")
tokenizer = context.get("tokenizer")
max_new_tokens = request.json.get("max_new_tokens", 512)
temperature = request.json.get("temperature", 0.7)
prompt = request.json.get("prompt")
prompt_template=f'''[INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:
{prompt}
[/INST]
'''
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=temperature, max_new_tokens=max_new_tokens)
result = tokenizer.decode(output[0])
return Response(json={"outputs": result}, status=200)
```
This example is from the `app.py` file in [CodeLlama-7B-Instruct-GPTQ](https://github.com/bananaml/demo-codellama-7b-instruct-gptq).
## LLM
```python
from langchain_community.llms import Banana
```
See a [usage example](/docs/integrations/llms/banana).
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