We describe ufakzeka-1, a 151M-parameter (182M with embeddings) decoder-only Turkish language model pretrained from scratch on 13.5B tokens of openly licensed text and instruction-tuned for chat, at a total cost of about \$286 in cloud GPU, API and notebook time.
The contribution is not the model's capability, which is what a model this size can be expected to have, but the record of building and measuring it:
- a Turkish byte-level tokenizer at 1.77 tokens per word,
- a three-stage pretraining schedule,
- a post-training mixture of openly licensed and generated data,
- and an evaluation battery of release gates, a rule-checked sweep of 5,508 conversations, judged conversations and hand tests, all with prompts held out from the training data, enforced by decontamination inside the data build and by a checked-in invariant script we run before each build.
We report three findings that we believe transfer to other small-model efforts:
- a safety gate that had been "fixed" with training data written from its own questions read 64/64 while the honest figure was 34/64;
- training-seed variance was as large as the spread across every recipe we tried, so single-seed comparisons at this scale are uninformative;
- and data rounds repaired only what was absent from the data, while identity tracking over long context and multi-turn arithmetic did not move across any data change we tried, which we read as limits of the model size rather than gaps in the data, a reading the next, larger model will test.
Weights, the data recipe, the evaluation code and the spend ledger are released under Apache-2.0.