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.

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Installation and Setup

  • Install the Python package with pip install gpt4all
  • Download a GPT4All model and place it in your desired directory
In this example, we are using mistral-7b-openorca.Q4_0.gguf: 
mkdir models
wget https://gpt4all.io/models/gguf/mistral-7b-openorca.Q4_0.gguf -O models/mistral-7b-openorca.Q4_0.gguf

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Usage

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GPT4All

To use the GPT4All wrapper, you need to provide the path to the pre-trained model file and the model’s configuration. 
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.invoke("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. 
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.invoke("Once upon a time, ", callbacks=callbacks)

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Model File

You can download model files from the GPT4All client. You can download the client from the GPT4All website. For a more detailed walkthrough of this, see this notebook