Rishiraj Acharya

I really like reading such detailed model reports that discuss these technical things like architecture, data & exact parameters that are so useful for ML Engineers. Loved the report even more than the model (which is good as well lol).

That was a great read, for anyone wanting a deeper understanding of the architecture, read https://huggingface.co/blog/rishiraj/matformer-in-gemma-3n

Thank you for the kind words, really glad you enjoyed the post!

We usually compute the forward KL:
KL(P‖Q), where P is the original (full-precision) model's output distribution and Q is the quantized model's.

This forward direction does emphasize mode covering — it penalizes cases where the quantized model assigns too little probability to tokens that the original model thought were likely. So yes, it's more forgiving if Q spreads its probabilities out, but punishes it when it "misses" key peaks from P.

So to your analogy: yes, when I talk about preserving the original model’s behavior, I am leaning into this “mode-covering” perspective. The idea is that we want the quantized model to remain attentive to the same likely outputs as the original. If it diverges too far — say, by ignoring a high-confidence token the full-precision model preferred — that’s where forward KL catches it (and where flips are more likely to show up).

One caveat: reverse KL (KL(Q‖P)) would behave very differently — it penalizes overconfidence in the quantized model that the original didn’t share, which could be useful too in some contexts, but it’s generally less stable in practice when P assigns low probabilities.