black_swan's sft18-qualified adapter for Qwen/Qwen2.5-Coder-1.5B-Instruct. It writes PyRel — a declarative Python query DSL — from a natural-language question and a schema.

71 MB. Serves Event Horizon, which runs Swan, PyRel and DuckDB as WebAssembly in the browser and needs generation from somewhere.

Executed, not string-matched: every prediction is run and its result compared with a verified reference, because PyRel has several correct spellings of most questions.

The two benchmarks disagree, and which one matters depends on your schema. Spider's are 2–7 concepts and ~279 characters; TPC-DS is 29 concepts and 31,208. This adapter is shipped for the wide case, where it leads by three questions — and it is the only one here that writes a working join against 29 concepts (2/6, against 0/6 for sft15). On small schemas sft15 is better by six.

Two other differences that do not show in the totals. It emits far fewer invalid programs — 1 against sft15's 5 on TPC-DS, 7 against 18 on Spider — so failures arrive as wrong answers rather than dead ends. And it acts on error corrections: feeding swan's translated error back through answer_question's retry loop repairs an invalid program, where sft15 ignored the same correction.

The adapter was trained and evaluated under plain greedy decoding, and several servers apply a repetition penalty and top-k sampling even at temperature 0. That mismatch measured as one wrong answer in five, and it reads as the model being weak rather than as a serving setting. Use do_sample=False, or pin top_k 1, top_p 1.0, repeat_penalty 1.0.

merge_and_unload() gives byte-identical output and is ~18% faster at long prompts, so merge at load time rather than serving the adapter unmerged.

Quantization is not free on a wide schema: q4_K_M costs ~5% relative on Spider and BIRD but 23% on TPC-DS. Prefer f16 where the memory allows.

It expects its own system prompt and a Schema: / Question: user turn, both in tools/local_generator.py (SYSTEM_V5, build_user_turn) and asserted byte-identical against the training split (train_spider_bird_v5.jsonl) by tools/verify_local_generator.py. A differently-worded system prompt is off-distribution and costs accuracy quietly.

Schemas render as Concept: prop, prop with foreign keys as Child.member -> Parent.

Joins on wide schemas, at 2/6. The traversal syntax is usually right and the property names are invented — sold_price for price, Demographics.county for Customer.address.county. Swan's error names the owning concept but not the path to it; swan#178 proposes adding that, which this model is well placed to use since it acts on corrections.