The tension at the heart of this project is familiar to anyone who has pushed a training tool past its original design. Nanochat got the model running, pretraining and supervised fine-tuning worked well. That matters. But a tool that excels at the first lap but stalls on interoperability is a tool that has traded long-term value for short-term convenience. The user's instinct to move toward the Llama architecture and the Transformers `Trainer` class is the right one, and for a clear reason: open-source projects live or die on accessibility.
A model that cannot be loaded with Transformers is a model that most of the community cannot use. Nanochat's auto-scaling depth parameter is a real advantage during experimentation, but it becomes a liability when the output resists standard tooling. The fact that the latest Nanochat version does not produce a Transformers-compatible model is not a minor inconvenience, it is a wall. Building a custom export script is possible, but it adds maintenance burden, introduces a single point of failure, and asks every potential contributor to trust a hand-rolled converter. That is not an open invitation. It is a toll booth.
The user has a larger dataset now and a stated goal of community access. Those two facts shift the priority from training convenience to output compatibility. Llama architecture is the proven path here, not because it is flashy, but because it is the common language of the open-source LLM ecosystem. Model weights saved in that format load into every major inference engine, every fine-tuning framework, and every evaluation pipeline. The auto-scaling depth trick is interesting, but it is not worth isolating the model from the ecosystem that gives it reach.
The concrete choice is clear: invest the effort in Llama and Transformers now, and never write that export script. The depth parameter is a nice shortcut, but shortcuts that lock your output into a proprietary format are not shortcuts, they are detours. Build on the architecture that lets others build on your work.