Comment by n0vella
4 days ago
Do you think these very small models have some utility in the real world? Apart from learning and academic purposes of course.
4 days ago
Do you think these very small models have some utility in the real world? Apart from learning and academic purposes of course.
Yes! To me the primary value is not just as a teaching or toy model. I see a lot o value in repeatable tasks if we think about enterprise and a local fast developer model for individual usage.
Here's some examples that are inspired by previous roles I had outside of Google, where a business I was working in needed real time text processing.
This tutorials were made with Gemma versions from a year ago, but could now be recreated with Gemma 270m
https://developers.googleblog.com/en/gemma-for-streaming-ml-... https://www.youtube.com/watch?v=YxhzozLH1Dk
If you LoRa them you can make them VERY VERY good at a small narrow set of tasks, e.g.:
- reply in a specific way, like a specific JSON schema, or in the voice of a character - be very good at classifying text (e.g. emails, or spam) - be a great summarizer for large amounts of text, e.g. turn emails into short titles or url slugs - adding tags/categories per your pre-defined rules (e.g. for communities, tagging content, marketing) - for detecting spam, or duplicates, or flagging things
You won't be able to write code or prose with these, but they're great for a huge array of very narrow set of use cases
What's neat about "stupid" models like this is that they're less likely to go off and dream up a bunch of irrelevant content, because they don't know much about the world / won't have too much context to pull from
Sure, interacting with natural language without expectation that the model contains knowledge. Good for things like tool use and embeddings where the information is all retrieved.
Are these small models are trained to privilege "raw intelligence" over factual knowledge? Is there any indication of how much of current model is dedicated to the knowledge of multiple languages and tons of facts rather than pure understanding and reasoning?
The evaluations provide this indication. You'll see MMLU, GPQA, Big Bench etc in reports for many models. Those numbers provide the indication you're looking for.
To answer a question you didn't ask. With small models especially we need to make choices as to which to focus on. For this model we focused on text summarization and instruction following, with the idea that users would finetune to gain performance on the task set that is relevant to them
It seems to be more often correct than wrong for multilingual translation tasks(source text from[1][2]). Rough, but probably useful as traveler's phrase books.
1: https://uk.wikipedia.org/wiki/%D0%A0%D0%BE%D1%88%D0%B5%D1%88...
2: https://vnexpress.net/lap-dien-mat-troi-mai-nha-tu-dung-co-t...
For comparison, here's what I got from the 27B variant: