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Comment by simonw | Hacker News Reader

Do you have any practical examples of fine-tuned variants of this that you can share? A description would be great, but a demo or even downloadable model weights (GGUF ideally) would be even better.

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We obviously need to create a pelican bicycle svg finetune ;) If you want to try this out I'd be thrilled to do it with you, I genuinely am curious how well this model can perform if specialized on that task.

A couple colleagues of mine posted an example of finetuning a model to take on persona's for videogame NPCs. They have experience working with folks in the game industry and a use case like this is suitable for game devs who want to start including lightweight models that won't take up a ton of accelerator memory and can run efficiently on CPU if needed. https://ai.google.dev/gemma/docs/core/huggingface_text_full_...

As for GGUF it's available here! https://huggingface.co/collections/ggml-org/gemma-3-270m-689...

Caves Of Qud uses Markov chain generated text to great effect in some places. I think something light that's still more competent than Markov chains has a lot of potential.

video game NPCs with intelligence :O gaming is going to be crazy

Do you know that hardware required to fine-tune this model? I'm asking on behave of us GPU starve folks

A free colab. Here's a link, you can finetune the model in ~5 minutes in this example, and I encourage you to try your own

https://ai.google.dev/gemma/docs/core/huggingface_text_full_...

Finally a Google guide using PyTorch and not Tensorflow, that alone made me wanting to try it out ;)

What size of tasks can this handle? Can you do a fine-tune of Mac System Settings?

32k context window so whatever fits in there. What is a finetune of mac system settings?

The finetune would be an LLM where you say something like "my colors on the screen look to dark" and then it points you to Displays -> Brightness. It feels like a relatively constrained problem like finding the system setting that solves your problem is a good fit for a tiny LLM.

This would be a great experiment. I'm not sure how the OS integration would work, but as a first pass you could try finetuning the model to take natural language "my colors on the screen look to dark" and then have it output "Displays -> Brightness", then expand to the various other paths you would like the model to understand

Maybe using a larger model to generate synthetic data of question path Combos, and also to rephrase and generate similar type questions for a more varier training set.

It seems to dip into repeating itself pretty quickly on any task of actual complexity.

I have so many game ideas that would use a small LLM built up in my brain, so thank you for this.

Now if only I could somehow fine tune my life to give me more free time.

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