GPT4All

GitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.

This example goes over how to use LangChain to interact with GPT4All models.

%pip install --upgrade --quiet  gpt4all > /dev/null

Note: you may need to restart the kernel to use updated packages.

Import GPT4All

from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import LLMChain
from langchain_community.llms import GPT4All
from langchain_core.prompts import PromptTemplate

API Reference:

• StreamingStdOutCallbackHandler
• LLMChain
• GPT4All
• PromptTemplate

Set Up Question to pass to LLM

template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate.from_template(template)

Specify Model

To run locally, download a compatible ggml-formatted model.

The gpt4all page has a useful Model Explorer section:

• Select a model of interest
• Download using the UI and move the .bin to the local_path (noted below)

For more info, visit https://github.com/nomic-ai/gpt4all.

---

local_path = (
    "./models/ggml-gpt4all-l13b-snoozy.bin"  # replace with your desired local file path
)

# Callbacks support token-wise streaming
callbacks = [StreamingStdOutCallbackHandler()]

# Verbose is required to pass to the callback manager
llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)

# If you want to use a custom model add the backend parameter
# Check https://docs.gpt4all.io/gpt4all_python.html for supported backends
llm = GPT4All(model=local_path, backend="gptj", callbacks=callbacks, verbose=True)

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

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

llm_chain.run(question)

Justin Bieber was born on March 1, 1994. In 1994, The Cowboys won Super Bowl XXVIII.