LLM

Articles about Large Language Models (LLM) such as GPT, Mistral or Mixtral. They show use cases, technical basics and practical tips for use in business.

LoRA training: How FileMaker 2025 simplifies the fine-tuning of large language models

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LoRA Fine tuning - FileMaker 2025

The world of artificial intelligence is on the move. New models, new methods and, above all, new possibilities are emerging on an almost weekly basis - and yet one thing remains constant: not every technical innovation automatically leads to a better everyday life. Many things remain experimental, complex or simply too costly for productive use. This is particularly evident in the so-called fine-tuning of large language models - a method of specializing generative AI to its own content, terms and tonalities.

I have accompanied this process intensively over the last few months - first in the classic form, with Python, terminal, error messages and nerve-wracking setup loops. And then: with FileMaker 2025. A step that surprised me - because it wasn't loud, but clear. And because it showed that there is another way.

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How AI specialists can be trained today - opportunities for companies and trainees

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Train an AI specialist

Just a few years ago, artificial intelligence was a topic for research institutes and large corporations. People talked about neural networks, deep learning and speech recognition - but it hardly played a role in everyday life. Today, AI is no longer a topic for the future, but a reality: it writes texts, creates images, analyzes data and controls production processes. Whether in administration, trade or industry - it can now be found everywhere.

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gFM-Business and the future of ERP: local intelligence instead of cloud dependency

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gFM-Business and AI + knowledge graph

For over a decade, the gFM-Business software has stood for something special in the German ERP market: it is not based on a cumbersome, difficult-to-maintain system, but on the lightweight, customizable and visually modelled FileMaker platform. This has many advantages: gFM-Business can be individually expanded, runs on Windows, macOS and iOS, and can be customized by both developers and ambitious power users.

With the advent of artificial intelligence (AI) - especially through so-called language models such as ChatGPT - new opportunities are now emerging that go far beyond traditional automation. gFM-Business is actively preparing for this future: with the aim of not only managing data, but also unlocking knowledge.

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Artificial intelligence: which jobs are at risk and how we can arm ourselves now.

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Which jobs will be eliminated by AI in the future

Hardly any other technological change has crept into our everyday lives as quickly as artificial intelligence. What was considered a visionary technology of the future yesterday is already a reality today - whether in texting, programming, diagnosing, translating or even creating music, art or legal briefs.

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MLX on Apple Silicon as local AI in comparison with Ollama & Co.

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Local AI on the Mac with MLX

At a time when centralized AI services such as ChatGPT, Claude or Gemini are dominating the headlines, many professional users are increasingly looking for an alternative - a local, self-controllable AI infrastructure. Especially for creative processes, sensitive data or recurring workflows, a local solution is often the more sustainable and secure option.

Anyone working with a Mac - especially with Apple Silicon (M1, M2, M3 or M4) - can now find amazingly powerful tools to run their own language models directly on the device. At the center of this is a new, largely unknown component: MLX, a machine learning framework developed by Apple that is likely to play an increasingly central role in the company's AI ecosystem in the coming years.

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RAG with Ollama and Qdrant as a universal search engine for own data

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Extend local AI with databases using RAG, Ollama and Qdrant

In an increasingly confusing world of information, it is becoming more and more important to make your own databases searchable in a targeted manner - not via classic full-text searches, but through semantically relevant answers. This is exactly where the principle of the RAG database comes into play - an AI-supported search solution consisting of two central components:

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Ollama meets Qdrant: A local memory for your AI on the Mac

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Memory for local AI with Ollama and Qdrant

Local AI with memory - without cloud, without subscription, without detour

In a previous articles I explained how to configure Ollama on the Mac install. If you have already completed this step, you now have a powerful local language model - such as Mistral, LLaMA3 or another compatible model that can be addressed via REST API.

However, the model only "knows" what is in the current prompt on its own. It does not remember previous conversations. What is missing is a memory.

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Local AI on the Mac: How to install a language model with Ollama

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Local AI on the Mac has long been practical - especially on Apple-Silicon computers (M series). With Ollama you get a lean runtime environment for many open source language models (e.g. Llama 3.1/3.2, Mistral, Gemma, Qwen). The current Ollama version now also comes with a user-friendly app that allows you to set up a local language model on your Mac at the click of a mouse. In this article you will find a pragmatic guide from installation to the first prompt - with practical tips on where things traditionally go wrong.

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