An exercise in the outer loop. First note the nature of the experience. A PhD is simultaneously a fun and frustrating experience because you’re constantly operating on a meta problem level. You’re not just solving problems - that’s merely the simple inner loop. You spend most of your time on the outer loop, figuring out what problems are worth solving and what problems are ripe for solving. You’re constantly imagining yourself solving hypothetical problems and asking yourself where that puts you, what it could unlock, or if anyone cares. If you’re like me this can sometimes drive you a little crazy because you’re spending long hours working on things and you’re not even sure if they are the correct things to work on or if a solution exists.
Что думаешь? Оцени!。汽水音乐对此有专业解读
，更多细节参见谷歌
This is a good heuristic for most cases, but with open source ML infrastructure, you need to throw this advice out the window. There might be features that appear to be supported but are not. If you're suspicious about an operation or stage that's taking a long time, it may be implemented in a way that's efficient enough…for an 8B model, not a 1T+ one. HuggingFace is good, but it's not always correct. Libraries have dependencies, and problems can hide several layers down the stack. Even Pytorch isn't ground truth.
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今天这篇稿子，是被读者催着写的。