GPT4All Nomic
winget install --id=nomic.gpt4all -e GPT4All is a GUI application for loading and interacting with Generative AI models
GPT4All is a graphical user interface (GUI) application designed to load and interact with Generative AI models locally on any device. It provides an accessible platform for running large language models (LLMs) without the need for cloud APIs or high-end hardware.
Key Features:
- Support for popular LLMs such as Mistral, LLaMA, and DeepSeek
- Cross-platform compatibility with Windows, macOS, Linux, and ARM architectures
- User-friendly interface with a redesigned chat application
- LocalDocs integration for private interaction with personal data
- Vulkan optimization for enhanced performance on NVIDIA and AMD GPUs
- Installable via winget
Audience & Benefit: Ideal for developers, researchers, and businesses seeking to integrate AI capabilities locally. GPT4All enables efficient, privacy-focused, and cost-effective use of LLMs directly on standard desktop or laptop hardware, eliminating the need for costly cloud infrastructure.
README
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.
https://github.com/nomic-ai/gpt4all/assets/70534565/513a0f15-4964-4109-89e4-4f9a9011f311
GPT4All is made possible by our compute partner Paperspace.
Download Links
Windows Installer
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macOS Installer
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Ubuntu Installer
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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.
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
- 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}},
}