Running GPT Locally with GPT4All

What is GPT4All ?

GPT4All is an open-source ecosystem for training and deploying large language models. The trained models can be executed on local desktop computers equipped with consumer-grade CPUs.

GPT4All is released under the permissive MIT license, allowing anyone to utilize it for training and running large language models. This ecosystem is maintained by Nomic AI.

However, it’s important to note that while the GPT4All ecosystem itself is released under the open-source MIT license, some of the specific trained models available on GPT4All’s website may have restrictions on commercial usage

GPT4All Clients

GPT4All provides desktop clients for all major operating systems, including Windows, macOS, and Linux.

The core backends of GPT4All are implemented in C/C++, but the platform also offers language binders for other programming languages. Presently, GPT4All supports Python, TypeScript, Golang, Java, and C#

Exploring GPT4All with Python

Install the GPT4All Python Library

pip install gpt4all

Download the GPT4All Model and Run the Sample Prompt

• Model Availability: The application checks for the presence of the model in the specified directory. If the model is not found, it will be automatically downloaded from the Internet.
• Model Size: The model utilized in this example has a size of approximately 1.9 GB.
• Pretrained Models: For a comprehensive list of pretrained models available for use, please refer to the GPT4All official website.

from gpt4all import GPT4All
model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin","D:/Study/gpt4all/model")
output = model.generate("Tell be about M.S.Dhoni?")
print(output)

GPT4All response

Conclusion

The open-source GPT4All opens the door to running LLM applications on local desktops without the need for powerful GPUs. However, it’s important to note that the accuracy of GPT4All’s responses depends on the specific trained model being used. The model employed in this article may not generate exceptionally impressive responses.

To unleash the true potential of GPT4All, incorporating a custom knowledge base through contextual information or training a model tailored to specific needs can significantly enhance its performance and capabilities. By doing so, GPT4All can become a more powerful and effective tool for various applications