Releasing an SFT framework alongside weights is a structural choice, not a packaging decision. When a model starts from GPT-2 initialization, its downstream fine-tunes inherit assumptions — tokenizer vocabulary, pretraining data distribution, architectural priors — that the original authors made. The r/LocalLLaMA post by Kill_Streak308 breaks that chain entirely: a custom 16k BPE tokenizer trained on WikiText-103 and TinyStories means every fine-tune built on this checkpoint owns its own lineage . That matters to a community whose central complaint about cloud AI is precisely the opacity of inherited decisions.