11 Building Locally-Running LLM-based Applications using GPT4All
This chapter covers
- Introducing GPT4All
- Loading a model from GPT4All
- Holding a conversation with a model from GPT4All
- Creating a Web UI for GPT4All using Gradio
You've learned about constructing LLM-based applications using models from OpenAI and HuggingFace. While these models have transformed natural language processing, there are notable drawbacks. Primarily, privacy emerges as a critical concern for businesses. Relying on third-party hosted models introduces a security risk, as your conversations would be transmitted to these external companies, raising apprehensions for businesses dealing with sensitive data. Additionally, the challenge of integrating these models with your private data exists, and even if accomplished, the initial privacy issue resurfaces.
A more effective approach is to execute the models locally on your computer. This allows you to have control over the destination of your private data and enables fine-tuning of the models to suit your specific data requirements. However, running a LLM often demands GPUs, constituting a significant investment. Fortunately, there's a remedy – GPT4All. GPT4All provides quantized models, reduced to just a few gigabytes, which can operate on standard consumer-grade CPUs without requiring an internet connection. Introduction to GPT4All