I'm seeing the same question come up about general performance versus specialized performance, so let me offer a longer explanation here. This might be worth a blog post at some point.
We now live in a world of both readily available small specialized models and general models.
In the last couple of years, we've seen an explosion of capability in generative models built and trained to be performant on a general set of capabilities. In Google's case, this model is Gemini. Gemini can summarize text, count the number of ducks in an image, generate a pelican SVG, play Pokemon, play chess, and do so many other things. It can do this all with a vague set of inputs across many modes. For models of this scale (many billion parameters), it's quite incredible how, with even vague or misspecified inputs, the computer can still produce useful results in complex scenarios.
However, there is an entire ecosystem of generative models that are purpose-built for ONE specific task. The ones I worked on are typically referred to as Bayesian models. These are models that can give probabilistic estimates of how many customers a restaurant will get in a day, or given penguin dimensions, predict the probability of penguin species, or models that take measurements from composite material testing and estimate if your airplane will stay together in flight. With models this size, it's incredible how a model with tens or hundreds of parameters can assist humans in making better decisions. I write about this specifically in PPL book I wrote a coupe years back. Chapter 9 provides the most "real world" workflow.
https://bayesiancomputationbook.com/markdown/chp_09.html
If you look through all the chapters you can see examples of forecasting models, bike sharing demand estimators, and all sorts of other narrow tasks. The tradeoff at this small scale, though, is the models have to be designed bespoke to your situation, and once you build one, it only works in that narrow task. No one expects to be handed a small Bayesian model that is already perfect at their task; it's implicit that users will bring their own data to update the model parameters.
So with this said, Gemma 270m is between these two paradigms. It's not at Gemini-level general performance and never will be. But it's not as rigid as an "old school" PPL-style Bayesian model where you need to make one by hand for every problem. However since it needs to be shaped to match specific tasks, we did our best to design it to be a flexible starting point for LLM-style tasks and worked with partners to put it into the right frameworks and places for you all to be able to shape it to what you need it to be. As the adage goes, consider it to be a tool in the toolbox between fully custom truly tiny generative models with 10 parameters and general generative models with lots of capability. Maybe not everyone needs this tool, but now you all have the choice.
Stepping aside from the technology for a moment, as a model builder and open ecosystem advocate, you never quite know how the community will receive these models until you release them. I genuinely appreciate you all commenting here; it helps me get a sense of what's working and what to focus on next.
And thanks for being kind about my typos in these answers. Trying to answer as many questions as possible across HN and various other forums.