This article discusses five ways to use large language models (LLMs) locally to maintain data privacy and easily generate context-aware responses. Using these models allows you to bypass online use, where your privacy may be compromised, and operate on your laptop without external tracking.
The first software is GPT4ALL, an open-source tool that facilities easy download and installation of large language models. Its automated system utilizes your GPU to generate quick responses, with the capacity to manage up to 30 tokens per second. GPT4ALL also has the ability to generate responses using Retrieval-Augmented Generation by accessing multiple folders with important data.
LM Studio is another software that offers some improvements over GPT4ALL. Despite being a closed source, it offers high-quality user interface and provides GPU offloading. LM Studio also allows local access to a plethora of open-source LLMs, acting much like OpenAI’s API. In addition, it offers an interactive user interface to modify your LLM’s responses.
Ollama is a command-line interface (CLI) tool that triggers quick operations for large language models such as Llama 2, Mistral, and Gemma. This tool is particularly useful for developers and can streamline the operation of various models.
The fourth software is LLaMA.cpp, a unique tool offering both a CLI and a Graphical User Interface (GUI). As it’s written in pure C/C++, it can handle high levels of customization and quickly respond to queries. LLaMA.cpp supports all types of operating systems and provides access to multimodal models like LLaVA, BakLLaVA, Obsidian, and ShareGPT4V.
The final tool discussed is NVIDIA Chat with RTX, which requires a 30 series or 40 series RTX NVIDIA graphics card with at least 8GB of RAM and 50GB of storage space. It has the potential to run LLaMA and Mistral models locally, and it can learn from various sources, including documents and YouTube videos.
The article concludes by suggesting starting with GPT4All and LM Studio to cover basic needs, then exploring Ollama and LLaMA.cpp, and finally, experimenting with Chat with RTX. Each of these tools have unique strengths and can be used to maintain privacy and generate personalized, context-aware responses to assist you in leveraging the latest LLMs.
