gpt4all/gpt4all-bindings/python/docs/index.md
mcembalest 5306595176
V3 docs max (#2488)
* new skeleton

Signed-off-by: Max Cembalest <max@nomic.ai>

* v3 docs

Signed-off-by: Max Cembalest <max@nomic.ai>

---------

Signed-off-by: Max Cembalest <max@nomic.ai>
2024-07-01 13:00:14 -04:00

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# GPT4All Documentation
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_desktop/quickstart.md#quickstart).
!!! note "Desktop Application"
GPT4All runs LLMs as an application on your computer. Nomic's embedding models can bring information from your local documents and files into your chats. It's fast, on-device, and completely **private**.
<div style="text-align: center; margin-top: 20px;">
[Download for Windows](https://gpt4all.io/installers/gpt4all-installer-win64.exe) &nbsp;&nbsp;&nbsp;&nbsp;
[Download for Mac](https://gpt4all.io/installers/gpt4all-installer-darwin.dmg) &nbsp;&nbsp;&nbsp;&nbsp;
[Download for Linux](https://gpt4all.io/installers/gpt4all-installer-linux.run)
</div>
!!! note "Python SDK"
Use GPT4All in Python to program with LLMs implemented with the [`llama.cpp`](https://github.com/ggerganov/llama.cpp) backend and [Nomic's C backend](https://github.com/nomic-ai/gpt4all/tree/main/gpt4all-backend). 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))
```