I feel like this is the true frontier, making smaller models that can do more than their predecessors. If we can crack this space to where you can get reasonable outputs from "mediocre hardware" it would be worthwhile, even if its somewhat inferior to frontier models, we can't forget that not long ago, frontier models are nowhere near as good as they are today, and tomorrow's models will likely be even better.
That's exactly what I had in mind. When I started this, I was jumping back and forth between this thought: "Can this model size actually generate logical English text?" and I played with a few different models of the same size and I was really really depressed when seeing how bad they are.... but then I discovered more and more tiny models and LaMini-125M, LaMini-256M, and nanowhale-100m, and SmolLM2-135M-Instruct are very very decent. So I decided to give it a try.
I've been working on something like this too, for quite a while! Though I'm trying to get a non-quadratic-attention LLM (or SLM) up and running.
And anyway, I think the most important thing is dataset quality. Dumping in whatever dataset you find on Huggingface is a recipe for mediocrity, so I'm also spending a lot of time on that.
In my case, I have a local branch where I'm experimenting with BitNet since it can run on a CPU too.
Qwen seems to be going in a good direction -- hundreds of experts on their MoE models. Extremely low active-weight counts while still performing quite admirably. I look forward to models with many, many more experts, to the point where anyone with enough random access can generate hundreds or thousands of tokens per second. Because right now, 80–120t/s is pretty slow.