Here is a project that guides you through it if you want to prove to yourself that it works https://github.com/arcee-ai/DistillKit

GPU kernel optimization is just the kind of well-bounded problem with clear success criteria that AI loves.

It's about evidence this is an active force in competition in LLMs.

It's also how providers build their smaller models out of their larger ones; they publicly talk about the process.

Make sure to stay updated!

If we wanted to compare model responses, we would give all models system prompts with model names, thereby fixing the Kimi misattribution.

The reason Kimi often states its name as Claude is likely because we can actually run it without the mandatory system prompt, smoothing over awkward competitor mentions.

[1] https://www.anthropic.com/news/detecting-and-preventing-dist...

I’m really not sure how you’re getting “exact” here.

They are crying about theft after committing the largest theft in human history.

I believe it can be used in the RL / fine tuning sense, in which case 150,000 requests, assuming every one was detected, could move the needle in quality.

I agree it couldn’t replace all of pre and post training , but I don’t think that’s the claim. You do typical training, then distill really difficult cases.