Ive gotten a lot of value lately using his writing to understand how parameter size, MOE and PLE variations on local models influence performance on various har

Ive gotten a lot of value lately using his writing to understand how parameter size, MOE and PLE variations on local models influence performance on various hardware. Its still a useful level of detail as someone who used to roll their own models for various applications pre-LLM but who now mostly uses LLMs rather than training them. If you used to write pytorch, i think his writing is a great level of technicality to stay on top of things. And id look to them if i ever wanted to do a bit of learning/train my own. Id love to hear if others have resources at a similar level of technicality and detial.