# Local LLM (GPT4All)

Source: https://github.com/nomic-ai/gpt4all

## Summary

GPT4All is a project that lets users run large language models privately on everyday desktops and laptops, without API calls or GPUs, by downloading an application. The download page lists installers for Windows, Windows ARM, macOS and Ubuntu, with the Windows and Linux builds requiring Intel Core i3 2nd Gen or AMD Bulldozer or better, and the macOS build requiring Monterey 12.6 or newer. A Python client built around llama.cpp implementations is also available, and the release history lists V3.0.0 on July 2nd, 2024, with a redesigned chat UI and expanded model support.

## Article

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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gpt4all_2.mp4

GPT4All is made possible by our compute partner Paperspace.

Download Links

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Windows Installer —

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Windows ARM Installer —

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macOS Installer —

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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.

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.

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

🦜🔗 Langchain 🗃️ Weaviate Vector Database - module docs 🔭 OpenLIT (OTel-native Monitoring) - Docs

Release History

July 2nd, 2024: V3.0.0 Release

Fresh redesign of the chat application UI

Improved user workflow for LocalDocs

Expanded access to more model architectures

October 19th, 2023: GGUF Support Launches with Support for:

Mistral 7b base model, an updated model gallery on our website, several new local code models including Rift Coder v1.5

Nomic Vulkan support for Q4_0 and Q4_1 quantizations in GGUF.

Offline build support for running old versions of the GPT4All Local LLM Chat Client.

September 18th, 2023: Nomic Vulkan launches supporting local LLM inference on NVIDIA and AMD GPUs.

July 2023: Stable support for LocalDocs, a feature that allows you to privately and locally chat with your data.

June 28th, 2023: Docker-based API server launches allowing inference of local LLMs from an OpenAI-compatible HTTP endpoint.

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}}, }