langchain/docs/ecosystem/gpt4all.md

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 pyllamacpp
• Download a GPT4All model and place it in your desired directory

Usage

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.llms import GPT4All

# Instantiate the model. Callbacks support token-wise streaming
model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, 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.

from langchain.llms import GPT4All
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
# There are many CallbackHandlers supported, such as
# from langchain.callbacks.streamlit import StreamlitCallbackHandler

callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8, callback_handler=callback_handler, verbose=True)

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

Model File

You can find links to model file downloads in the pyllamacpp repository.

For a more detailed walkthrough of this, see this notebook