Fine-tuned from LiquidAI/d1-omni-600M to select one of four supplied SQL queries using a schema, question, and conversation history. Your application supplies and executes the queries. On 1,000 synthetic development turns, v1 scored 45.6% versus 24.9% for the unchanged base, a gain of 20.7 percentage points. Both used FP32, complete inputs, original query order, batch size 4, and highest-score selection. The set contains 250 four-turn conversations across 10 synthetic clinical task families excluded from training. Each turn receives a fixed supplied history, independent of earlier predictions. This development set was reused for model selection. Results do not establish independent-test or…