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Best results with Apple Silicon M-series processors.\n\u003c/p\u003e\n\nSee the full [System Requirements](gpt4all-chat/system_requirements.md) for more details.\n\n\u003cbr/\u003e\n\u003cbr/\u003e\n\u003cp\u003e\n  \u003ca href='https://flathub.org/apps/io.gpt4all.gpt4all'\u003e\n    \u003cimg style=\"height: 2em; width: auto\" alt='Get it on Flathub' src='https://flathub.org/api/badge'\u003e\u003cbr/\u003e\n    Flathub (community maintained)\n  \u003c/a\u003e\n\u003c/p\u003e\n\n## Install GPT4All Python\n\n`gpt4all` gives you access to LLMs with our Python client around [`llama.cpp`](https://github.com/ggerganov/llama.cpp) implementations. \n\nNomic contributes to open source software like [`llama.cpp`](https://github.com/ggerganov/llama.cpp) to make LLMs accessible and efficient **for all**.\n\n```bash\npip install gpt4all\n```\n\n```python\nfrom gpt4all import GPT4All\nmodel = GPT4All(\"Meta-Llama-3-8B-Instruct.Q4_0.gguf\") # downloads / loads a 4.66GB LLM\nwith model.chat_session():\n    print(model.generate(\"How can I run LLMs efficiently on my laptop?\", max_tokens=1024))\n```\n\n\n## Integrations\n\n:parrot::link: [Langchain](https://python.langchain.com/v0.2/docs/integrations/providers/gpt4all/)\n:card_file_box: [Weaviate Vector Database](https://github.com/weaviate/weaviate) - [module docs](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-gpt4all)\n:telescope: [OpenLIT (OTel-native Monitoring)](https://github.com/openlit/openlit) - [Docs](https://docs.openlit.io/latest/integrations/gpt4all)\n\n## Release History\n- **July 2nd, 2024**: V3.0.0 Release\n    - Fresh redesign of the chat application UI\n    - Improved user workflow for LocalDocs\n    - Expanded access to more model architectures\n- **October 19th, 2023**: GGUF Support Launches with Support for:\n    - Mistral 7b base model, an updated model gallery on our website, several new local code models including Rift Coder v1.5\n    - [Nomic Vulkan](https://blog.nomic.ai/posts/gpt4all-gpu-inference-with-vulkan) support for Q4\\_0 and Q4\\_1 quantizations in GGUF.\n    - Offline build support for running old versions of the GPT4All Local LLM Chat Client.\n- **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.\n- **July 2023**: Stable support for LocalDocs, a feature that allows you to privately and locally chat with your data.\n- **June 28th, 2023**: [Docker-based API server] launches allowing inference of local LLMs from an OpenAI-compatible HTTP endpoint.\n\n[Docker-based API server]: https://github.com/nomic-ai/gpt4all/tree/cef74c2be20f5b697055d5b8b506861c7b997fab/gpt4all-api\n\n## Contributing\nGPT4All welcomes contributions, involvement, and discussion from the open source community!\nPlease see CONTRIBUTING.md and follow the issues, bug reports, and PR markdown templates.\n\nCheck project discord, with project owners, or through existing issues/PRs to avoid duplicate work.\nPlease make sure to tag all of the above with relevant project identifiers or your contribution could potentially get lost.\nExample tags: `backend`, `bindings`, `python-bindings`, `documentation`, etc.\n\n## Citation\n\nIf you utilize this repository, models or data in a downstream project, please consider citing it with:\n```\n@misc{gpt4all,\n  author = {Yuvanesh Anand and Zach Nussbaum and Brandon Duderstadt and Benjamin Schmidt and Andriy Mulyar},\n  title = {GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo},\n  year = {2023},\n  publisher = {GitHub},\n  journal = {GitHub repository},\n  howpublished = {\\url{https://github.com/nomic-ai/gpt4all}},\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnomic-ai%2Fgpt4all","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnomic-ai%2Fgpt4all","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnomic-ai%2Fgpt4all/lists"}