Very coherent for the size, though it is also adamant in defending its wrong conclusions and false knowledge
I found the model to be pretty smart for being so small in size, it's remarkable coherent and it is multi-lingual - similar maybe to A1 in language knowledge.
I doubt that reasoning training for such small models is very helpful, I think they are better in non reasoning tasks.
Reasoning certainy will make them smarter but they stay so dumb that it makes no difference in real world use.
The major flaw I found was that the model is insisting on wrong knowledge.
When training a tiny llm I believe you should change the approach, the model needs to be aware that it is very "uncertain" bout literally everything.
For example, Qwen 0.6B (and also some larger versions) claims that Einstein died in Germany, never was in the USA and it actually got the date correct.
However if you then ask if how einstein "survived" the WW2 in Germany, Qwen says that he died before WW2 heated up.
Asking it for dates it will show correct dates for WW2 and for Einstein but it will always defend the conclusion that Einstein died before.
While this might be solveable with reasoning, the underlying flaw is that certainty it is being trained for during fine tuning.
I think it would be smart to try add higher levels of uncertainty into tiny models during post training.
Their natural response should not be certain which might also make it less prone to defend wrong knowledge or bad logic.