langchain/docs/versioned_docs/version-0.2.x/integrations/providers/gpt4all.mdx

# GPT4All

This page covers how to use the `GPT4All` wrapper within LangChain. The tutorial is divided into two parts: installation and setup, followed by usage with an example.

## Installation and Setup

- Install the Python package with `pip install gpt4all`
- Download a [GPT4All model](https://gpt4all.io/index.html) and place it in your desired directory

In this example, We are using `mistral-7b-openorca.Q4_0.gguf`(Best overall fast chat model):

```bash
mkdir models
wget https://gpt4all.io/models/gguf/mistral-7b-openorca.Q4_0.gguf -O models/mistral-7b-openorca.Q4_0.gguf
```

## Usage

### GPT4All

To use the GPT4All wrapper, you need to provide the path to the pre-trained model file and the model's configuration.

```python
from langchain_community.llms import GPT4All

# Instantiate the model. Callbacks support token-wise streaming
model = GPT4All(model="./models/mistral-7b-openorca.Q4_0.gguf", n_threads=8)

# Generate text
response = model("Once upon a time, ")
```

You can also customize the generation parameters, such as n_predict, temp, top_p, top_k, and others.

To stream the model's predictions, add in a CallbackManager.

```python
from langchain_community.llms import GPT4All
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

# There are many CallbackHandlers supported, such as
# from langchain.callbacks.streamlit import StreamlitCallbackHandler

callbacks = [StreamingStdOutCallbackHandler()]
model = GPT4All(model="./models/mistral-7b-openorca.Q4_0.gguf", n_threads=8)

# Generate text. Tokens are streamed through the callback manager.
model("Once upon a time, ", callbacks=callbacks)
```

## Model File

You can find links to model file downloads in the [https://gpt4all.io/](https://gpt4all.io/index.html).

For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/gpt4all)