nomic-ai /
GPT4All: запуск локальных LLM на любом устройстве. Open-source решение, доступное для коммерческого использования.
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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))
:parrot::link: Langchain :card_file_box: Weaviate Vector Database - module docs :telescope: OpenLIT (OTel-native Monitoring) - Docs
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}},
}
Данные обновлены: 24 марта 2026 г.