GPT4All

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

GPT4All is made possible by our compute partner Paperspace.

## Install GPT4All Python `gpt4all` gives you access to LLMs with our Python client around [`llama.cpp`](https://github.com/ggerganov/llama.cpp) implementations. Nomic contributes to open source software like [`llama.cpp`](https://github.com/ggerganov/llama.cpp) to make LLMs accessible and efficient **for all**. ```bash pip install gpt4all ``` ```python 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)) ``` ### Release History - **July 2nd, 2024**: V3.0.0 Release - New UI/UX: fresh redesign of the chat application GUI and user experience - LocalDocs: bring information from files on-device into chats - **October 19th, 2023**: GGUF Support Launches with Support for: - Mistral 7b base model, an updated model gallery on [gpt4all.io](https://gpt4all.io), several new local code models including Rift Coder v1.5 - [Nomic Vulkan](https://blog.nomic.ai/posts/gpt4all-gpu-inference-with-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](https://blog.nomic.ai/posts/gpt4all-gpu-inference-with-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. [Docker-based API server]: https://github.com/nomic-ai/gpt4all/tree/cef74c2be20f5b697055d5b8b506861c7b997fab/gpt4all-api ### Integrations * :parrot::link: [Langchain](https://python.langchain.com/v0.2/docs/integrations/providers/gpt4all/) * :card_file_box: [Weaviate Vector Database](https://github.com/weaviate/weaviate) - [module docs](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-gpt4all) * :telescope: [OpenLIT (OTel-native Monitoring)](https://github.com/openlit/openlit) - [Docs](https://docs.openlit.io/latest/integrations/gpt4all) ## 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. ## Technical Reports

:green_book: Technical Report 3: GPT4All Snoozy and Groovy

:green_book: Technical Report 2: GPT4All-J

:green_book: Technical Report 1: GPT4All

## 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}}, } ```