
gpt4all
GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
Stars: 72866

GPT4All is an ecosystem to run powerful and customized large language models that work locally on consumer grade CPUs and any GPU. Note that your CPU needs to support AVX or AVX2 instructions. Learn more in the documentation. A GPT4All model is a 3GB - 8GB file that you can download and plug into the GPT4All open-source ecosystem software. Nomic AI supports and maintains this software ecosystem to enforce quality and security alongside spearheading the effort to allow any person or enterprise to easily train and deploy their own on-edge large language models.
README:
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.
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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)
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))
🦜🔗 Langchain 🗃️ Weaviate Vector Database - module docs 🔭 OpenLIT (OTel-native Monitoring) - Docs
-
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.
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.
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}},
}
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