As much as I'd like this to work, 1-bit / ternary models like Bonsai are (today) a dud.
The sweet spot appears to be ~4 to 5 bits per parameter. That's what @UnslothAI find to be the "knee" of the pareto curve (thanks @realGeorgeHotz for pointing this out).
Interestingly a synapse in the human brain uses about 4.7 bits of information so perhaps this points to something more fundamental.
You're much better off using a smaller model quantized to 4-bit than a larger model quantized to 1-bit or ternary.
We also find these 1-bit / ternary models to be overly verbose - so they can actually run *slower* on end to end tasks than 4-bit, even though the decode TPS is faster.