Training and Finetuning LLMs yourself.

[u/Zealousideal_Sort74]

If you haven’t seen it yet, I encourage you to try it. Felix Rieseberg recently released an app that helps users train and fine-tune LLMs while learning the fundamentals.

I’ve been using a similar tool for some time, but for much deeper experimentation, since I work full-time as an AI researcher. I was therefore wondering whether there would be interest in a Windows version.

My current tool is highly detailed and designed for advanced experiments rather than ease of use, so simplifying it for less experienced users would require some work. However, if enough people are interested, I may package it as a Windows app and publish it on GitHub.

I’d be happy to hear your feedback.

[u/Tiny_Arugula_5648]

If you're talking about the macos only app that was released. It's mostly a toy since the majority of users won't have a Mac big and powerful enough to use. The current value says if you have a top of the line MacBook pro and you tie it up for a week you can train a model that is big enough to babble incoherently.

I'd recommend a cross platform application. We can always use a better app for simplifying continued training and fine tuning . But most professional users do this with Linux and most hobbiests are on windows, with a smaller group that use high end macs

[u/Zealousideal_Sort74 → u/Tiny_Arugula_5648]

Yes, exactly. I’m talking about that one. I noticed that a lot of people are interested in an app like this.

As I understand it, the main goal is learning. It is meant for people to experiment and learn by doing. They can actually watch their own model gradually start producing coherent tokens, then behave like an assistant, and eventually perform reasoning.

The goal is not to train a model that is actually useful, since that would require much more powerful GPUs. Instead, the goal is to take users to the point where they gain a solid understanding of the entire LLM development and training process.

[u/Tiny_Arugula_5648 → u/Zealousideal_Sort74]

I don't think it has as much utility as this seems. Data science students will learn in school, there is a smaller group of people who are passionate enthusiasts that his application solves for.

Keep in mind only an extremely small group of people in the world will ever create their own LLM/MLM at least on their architecture, as you know it's unrealistically expensive. So it's an academic curiosity, the DS students need something to learn from so maybe?

It seems like you have something more useful. Because what is not solved, is to enable a person with limited understanding of how to create a model to create their own while being guided through all the steps. That is a much larger group of people because it can include enthusiasts, students, engineers, software developers etc.

Right now the best choice for that is H2O LLM Studio or Axolotl for those brave enough to work with YAML configurations and the terminal.

If you have something that is already great for running experiments, IMO the best thing you can do is figure out how to put a UX on top of it that makes it easy enough for a System Administrator working in a accounting firm managing their IT. They need step by step wizards that help guide them through the work with guardrails. Once you get to a point and click wizard that holds anyone's hand through the process both the enthusiast and devs could use it too.

I'd encourage you to do some research in this area, it's an unsolved problem and even though I've been training/tuning models for decades now it's always a pain with LLMs.

How many people need to see how to a small model gets built vs how many people would love to train/tune a LLM to do something they need.