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* 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>
29 lines
1.5 KiB
Markdown
29 lines
1.5 KiB
Markdown
# GPT4All Documentation
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GPT4All runs large language models (LLMs) privately on everyday desktops & laptops.
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No API calls or GPUs required - you can just download the application and [get started](gpt4all_desktop/quickstart.md#quickstart).
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!!! note "Desktop Application"
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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**.
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<div style="text-align: center; margin-top: 20px;">
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[Download for Windows](https://gpt4all.io/installers/gpt4all-installer-win64.exe)
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[Download for Mac](https://gpt4all.io/installers/gpt4all-installer-darwin.dmg)
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[Download for Linux](https://gpt4all.io/installers/gpt4all-installer-linux.run)
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</div>
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!!! note "Python SDK"
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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**.
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```bash
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pip install gpt4all
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```
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```python
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from gpt4all import GPT4All
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model = GPT4All("Meta-Llama-3-8B-Instruct.Q4_0.gguf") # downloads / loads a 4.66GB LLM
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with model.chat_session():
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print(model.generate("How can I run LLMs efficiently on my laptop?", max_tokens=1024))
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```
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