Congratulations. 28MB is impressive, I've not played around with actual queries/outputs.

I'm wondering what is the overall thesis/plan here and where exactly the innovation lies? Would love if you can throw light on below,

  - If I understand, this is complete stack of a custom architecture (attention only transformers), custom quantisation format and a runtime engine all packaged together?
  - How do you differentiate / compete against LiteRT (former TensorFlowLite) and Lite RT LM? Google is heavily investing in this ecosystem because Android is where they have distribution moat. Wouldn't it be easier for me as a developer to build on top of LiteRT since it is relatively open ecosystem and I can pack large number of open models from HF directly?
  - What exact challenges you saw with TFLite, TVM etc that prompted this effort ?
  - What will be the pricing model like?