Today we discussed the concept of private LLMs (Large Language Models) and how they work. It is crucial to understand the setup of open-source LLMs, as it ensures that intelligence remains on-premises within our infrastructure, maintaining data privacy.
We explored how Ollama, an open-source tool, enables the running of large language models transparently and with public access. This differs from private companies like OpenAI, who do not disclose their models. By utilizing open-source LLMs, we gain transparency, understanding, and flexibility in our data processing.
Various models, such as Llama 3.1 and P3 Mini, offer different parameters and efficiency levels, even on mobile devices. We learned how to embed private data into the model to enhance its performance, without sharing data with third parties.
Ultimately, a private LLM setup involves installing Ollama, downloading open-source models, and running them on a private server. By embedding private data into the model and using a mini-database for storage, we ensure data privacy and control over the process.
I hope you found this explanation clear and interesting. If you have any questions or need assistance setting up private LLMs for your data processing needs, feel free to reach out to nowtec solutions AG. Thank you for your attention, and have a great day!