gpt4all README

The project's README from github.com/nomic-ai/gpt4all. As one markdown file: README.md.

GPT4All

Now with support for DeepSeek R1 Distillations

Website • Documentation • Discord • YouTube Tutorial

GPT4All runs large language models (LLMs) privately on everyday desktops & laptops.

No API calls or GPUs required - you can just download the application and get started.

Read about what's new in our blog.

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https://github.com/nomic-ai/gpt4all/assets/70534565/513a0f15-4964-4109-89e4-4f9a9011f311

GPT4All is made possible by our compute partner Paperspace.

— Windows Installer —

— Windows ARM Installer —

— macOS Installer —

— Ubuntu Installer —

The Windows and Linux builds require Intel Core i3 2nd Gen / AMD Bulldozer, or better.

The Windows ARM build supports Qualcomm Snapdragon and Microsoft SQ1/SQ2 processors.

The Linux build is x86-64 only (no ARM).

The macOS build requires Monterey 12.6 or newer. Best results with Apple Silicon M-series processors.

See the full System Requirements for more details.



Get it on Flathub
Flathub (community maintained)

Install GPT4All Python

gpt4all gives you access to LLMs with our Python client around llama.cpp implementations.

Nomic contributes to open source software like llama.cpp to make LLMs accessible and efficient for all.

pip install gpt4all
from gpt4all import GPT4All
model = GPT4All("Meta-Llama-3-8B-Instruct.Q4_0.gguf") # downloads / loads a 4.66GB LLM
with model.chat_session():
    print(model.generate("How can I run LLMs efficiently on my laptop?", max_tokens=1024))

Integrations

:parrot::link: Langchain :card_file_box: Weaviate Vector Database - module docs :telescope: OpenLIT (OTel-native Monitoring) - Docs

Release History

Contributing

GPT4All welcomes contributions, involvement, and discussion from the open source community! Please see CONTRIBUTING.md and follow the issues, bug reports, and PR markdown templates.

Check project discord, with project owners, or through existing issues/PRs to avoid duplicate work. Please make sure to tag all of the above with relevant project identifiers or your contribution could potentially get lost. Example tags: backend, bindings, python-bindings, documentation, etc.

Citation

If you utilize this repository, models or data in a downstream project, please consider citing it with:

@misc{gpt4all,
  author = {Yuvanesh Anand and Zach Nussbaum and Brandon Duderstadt and Benjamin Schmidt and Andriy Mulyar},
  title = {GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/nomic-ai/gpt4all}},
}

gpt4all (nomic-ai/gpt4all)

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77.4K ·GitHub
What is this?GPT4All is a desktop application that runs large language models privately on everyday laptops and desktops, with no API calls or GPU required.
Is this a chat app or a library I import into code?Both: there is a native desktop chat application with installers, plus a Python client (gpt4all package) for programmatic access to the same local LLMs.
How do I get it running?Download the prebuilt installer for your OS (Windows, Windows ARM, macOS, or Ubuntu) and run it, or install the gpt4all Python package to use it from code.
Does every OS build support the same hardware?The Linux build is x86-64 only with no ARM support, the Windows ARM build only works on Qualcomm Snapdragon or Microsoft SQ1/SQ2 chips, and the macOS build needs Monterey 12.6 or newer (best on Apple Silicon).
Who is this for, and is commercial use allowed?It targets anyone who wants private, local LLM chat on their own machine, and it is explicitly open-source and licensed for commercial use.
Which platforms are supported?Native installers exist for Windows, Windows ARM, macOS, and Ubuntu Linux.
Does my chat data stay on my machine?Yes, GPT4All runs LLMs privately on your device and even lets you chat with your own documents locally via the LocalDocs feature.
What model formats and architectures does it support?GPT4All supports GGUF-format models and has expanded support for multiple model architectures, including models like Mistral 7B and local code models such as Rift Coder.
Can I expose it as an API server?Yes, there is a Docker-based API server that exposes local LLM inference through an OpenAI-compatible HTTP endpoint.
Does it integrate with other AI tooling?Yes, it integrates with Langchain, the Weaviate vector database, and OpenLIT for OpenTelemetry-native monitoring.
Why doesn't it need GPUs to run LLMs?It uses llama.cpp-based implementations, which the project contributes to, to make LLM inference efficient enough to run on everyday CPUs.
Creator
nomic-ai
Size
43.6 MB
Stars
77.4K
Age
3y
Commits · 90 Days