nomic-ai/ gpt4all
View on GitHubGPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
https://github.com/nomic-ai/gpt4all/assets/70534565/513a0f15-4964-4109-89e4-4f9a9011f311
See the full System Requirements for more details.
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}},
}
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