# Modal

This page covers how to use the Modal ecosystem to run LangChain custom LLMs.
It is broken into two parts: 

1. Modal installation and web endpoint deployment
2. Using deployed web endpoint with `LLM` wrapper class.

## Installation and Setup

- Install with `pip install modal`
- Run `modal token new`

## Define your Modal Functions and Webhooks

You must include a prompt. There is a rigid response structure:

```python
class Item(BaseModel):
    prompt: str

@stub.function()
@modal.web_endpoint(method="POST")
def get_text(item: Item):
    return {"prompt": run_gpt2.call(item.prompt)}
```

The following is an example with the GPT2 model:

```python
from pydantic import BaseModel

import modal

CACHE_PATH = "/root/model_cache"

class Item(BaseModel):
    prompt: str

stub = modal.Stub(name="example-get-started-with-langchain")

def download_model():
    from transformers import GPT2Tokenizer, GPT2LMHeadModel
    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
    model = GPT2LMHeadModel.from_pretrained('gpt2')
    tokenizer.save_pretrained(CACHE_PATH)
    model.save_pretrained(CACHE_PATH)

# Define a container image for the LLM function below, which
# downloads and stores the GPT-2 model.
image = modal.Image.debian_slim().pip_install(
    "tokenizers", "transformers", "torch", "accelerate"
).run_function(download_model)

@stub.function(
    gpu="any",
    image=image,
    retries=3,
)
def run_gpt2(text: str):
    from transformers import GPT2Tokenizer, GPT2LMHeadModel
    tokenizer = GPT2Tokenizer.from_pretrained(CACHE_PATH)
    model = GPT2LMHeadModel.from_pretrained(CACHE_PATH)
    encoded_input = tokenizer(text, return_tensors='pt').input_ids
    output = model.generate(encoded_input, max_length=50, do_sample=True)
    return tokenizer.decode(output[0], skip_special_tokens=True)

@stub.function()
@modal.web_endpoint(method="POST")
def get_text(item: Item):
    return {"prompt": run_gpt2.call(item.prompt)}
```

### Deploy the web endpoint

Deploy the web endpoint to Modal cloud with the [`modal deploy`](https://modal.com/docs/reference/cli/deploy) CLI command.
Your web endpoint will acquire a persistent URL under the `modal.run` domain.

## LLM wrapper around Modal web endpoint

The  `Modal` LLM wrapper class which will accept your deployed web endpoint's URL.

```python
from langchain_community.llms import Modal

endpoint_url = "https://ecorp--custom-llm-endpoint.modal.run"  # REPLACE ME with your deployed Modal web endpoint's URL

llm = Modal(endpoint_url=endpoint_url)
llm_chain = LLMChain(prompt=prompt, llm=llm)

question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"

llm_chain.run(question)
```

