Introducing GPT4All
Tags: chatbot, llm, rag, gpt4all
Published: 2023-09-18 11:04:36

> Welcome to my new series of articles about AI called _**Bringing AI Home**_. It explores open source options for desktop and private self-hosted use of Artificial Intelligence and more specifically Large Language Models and AI Assistants.

[GPT4All](https://gpt4all.io) stands out as an open-source software that promises accessibility and privacy. It's built for those who desire a robust ChatBot that operates locally without compromising on the user's privacy or requiring sophisticated hardware setups.

![GPT4All Chat](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/kv5nz4neuu4pudhlgo4u.gif)

The main features of GPT4All are:

- **Local & Free**: Can be run on local devices without any need for an internet connection.
- **Hardware Friendly**: Specifically tailored for consumer-grade CPUs, making sure it doesn't demand GPUs. However, ensure your CPU is AVX or AVX2 instruction supported.
- **Optimized**: Efficiently processes 3-13 billion parameter large language models on laptops, desktops, and servers.
- **Compact**: The GPT4All models are just a 3GB - 8GB files, making it easy to download and integrate.

## Ecosystem

The components of the GPT4All project are the following:

1. **GPT4All Backend**: This is the heart of GPT4All. It holds and offers a universally optimized C API, designed to run multi-billion parameter Transformer Decoders. This foundational C API can be extended to other programming languages like C++, Python, Go, and more.
   
2. **GPT4All Bindings**: Houses the bound programming languages, including the Command Line Interface (CLI).
   
3. **GPT4All API**: Still in its early stages, it is set to introduce REST API endpoints, which will aid in fetching completions and embeddings from the language models.

4. **GPT4All Chat**: A native application designed for macOS, Windows, and Linux. It brings GPT4All's capabilities to users as a chat application. Furthermore, it's enhanced with plugins like LocalDocs, allowing users to converse with their local files ensuring privacy and security.

5. **GPT4All Datasets**: An initiative by Nomic AI, it offers a platform named [Atlas](https://huggingface.co/datasets/nomic-ai/gpt4all-j-prompt-generations) to aid in the easy management and curation of training datasets.
   
6. **GPT4All Open Source Datalake**: A transparent [space](https://github.com/nomic-ai/gpt4all-datalake) for everyone to share assistant tuning data. This democratic approach lets users contribute to the growth of the GPT4All model. Using the chat client, users can opt to share their data; however, privacy is prioritized, ensuring no data is shared without the user's consent.

The power of GPT4All resides in its multi models support. Currently, there are six different model architectures that are supported:

- EleutherAI's [GPT-J](https://www.eleuther.ai/artifacts/gpt-j)
- Meta's [LLaMA](https://ai.meta.com/llama/)
- Mosaic ML's [MPT](https://www.mosaicml.com/blog/mpt-7b)
- [Replit](https://huggingface.co/replit/replit-code-v1-3b)
- TII's [Falcon](https://falconllm.tii.ae/)
- BigCode's [StarCoder](https://huggingface.co/bigcode/starcoder)

These architectures see frequent updates, ensuring optimal performance and quality. Specifically, the GPTJ and MPT models have shown competitive performance against LLaMA, and innovations within the MPT models hint at promising future developments.

The GPT4All opens source project, powered and maintained by [Nomic AI](https://home.nomic.ai/), ensures that the chatbot world receives a system that guarantees quality, security, and local operation. Its multiple components and support for various architectures make it a versatile tool for both individuals and enterprises. 

## GPT4All in action

Let's install and use GPT4All Chat on a Windows Desktop!

The installation is pretty straightforward since a all-in-one installer is provided [here](https://gpt4all.io/installers/gpt4all-installer-win64.exe).
Just note that we will need between 8 and 16 GB RAM memory depending on the models you plan to use in GPT4All.

![Install models locally](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ci4s4bh1xgj7jnhw4ijh.png)

After selecting and installing models you can switch between them from the chat window.

![Select model to use](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ujnro8x94rdxe63pesed.png)

Let's select the Falcon model and ask it a question on RAG. Depending on your Dekstop's hardware configuration the answer might take some time.

![Prompt with Falcon model](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/y43abuhuetlj0wwq89zx.png)

Now let's implement RAG itself with GPT4All by configuring the LocalDocs plugin. It is done by declaring the local folders containing the documents to be indexed and used for RAG.

![LocalDocs Plugin configuration](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/pbskokvn9x9uqd7mn9gl.png)

It is then possible to tell GPT4All which repository to use to contextualize its answers. Here I chose my Yoga repository.

![LocalDocs activation](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/a2k8fdyhj9bt2vkqundk.png)

And switch to LLaMA model and ask it a question regarding Yoga. We can observe that GPT4All is retrieving and then processing contextual information from my Yoga repository.

![LocalDocs in action](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/4ktlpr4nhqyh2mspzlj1.png)

It then produces its contextual answer and gives the local references it used!

![Chat with LLaMA and LocalDocs](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ok85hkhbmcu3yiqtrmy3.png)

## Conclusion

GPT4All brilliantly bridges advanced AI with local operation, democratizing state-of-the-art chatbot experiences without sacrificing privacy or efficiency. Its adaptability, showcased through diverse model support and plugins like LocalDocs, redefines user-centered AI communication. 

With GPT4All, you're not just getting a chatbot; you're harnessing a locally-operated, contextually-aware, and powerfully efficient AI assistant.