This "experiment" has become very popular for its lulz-quotient, but to me it highlights a different problem @huggingface will need to contend with -- hub pollution. If you look at the model's README, it doesn't say this is a satire or joke model, nor does it say what the model actually produces. In an era where AI agents are increasingly navigating the HF hub and using models/datasets in their pipelines, spammy datasets/models poison the hub, and there has to be a way to engineer trust into these open source artifacts. This is more important now than ever, because there are deep-pocketed adversaries who would like to see open source AI become untrustworthy. I am confident that the leadership of @ClementDelangue, @julien_c and @Thom_Wolf, and the broader community, will solve this, but in the meantime we have some work cut out for us (and our agents)!
we distilled 2.3M Claude Fable 5 reasoning traces into Qwen3-4B
- 100% self-consistency @ 512 samples - 0.00 bits output entropy - zero hallucination variance
turns out the student is not bounded by the teacher. it also converged on one universal truth.
we open-sourced the model weights馃憞



