Active research
Hime
A translation pipeline for long-form literary Japanese into English. Several small fine-tuned models per paragraph instead of one large model in a data centre — running on a single consumer GPU, fully offline, EPUB in and EPUB out.
The pipeline
Each paragraph passes through four stages. The interesting part is not the chain — it is that retrieval feeds every stage, not just the first one. Glossaries and earlier passages are pulled in per paragraph, which is what keeps names, honorifics and register stable across hundreds of pages.
Where it currently stands
The fine-tuned models do not yet reliably beat their own base model. The most recent supervised fine-tune measured as a regression against the previous one. The diagnosis is that the ceiling sits in the base model and in the training data rather than in the adapter, which is why the next step is continued pretraining rather than more fine-tuning.
Stating that plainly costs nothing. A pipeline that only ever reported improvements would be less believable, not more.
Setup
- single consumer GPU
- fine-tuned adapters
- retrieval-augmented context
- fully offline
The part that works
A result was withdrawn after the harness caught its own leak.
The evaluation infrastructure is the mature part of this project: a de-leaked judge harness with 5 integrity gates, and a probe covering 41 linguistic axes, scored by a panel of reader personas.
An earlier positive result was traced to a leak in the answer key and retracted. It is recorded here rather than quietly dropped, because a measurement setup that cannot catch its own mistakes is not a measurement setup.
The repository is private, so there is no link to it here. A link that returns 404 is worse than none.