Normalize Queries with AST Parsing for Accurate Evaluation

String mismatches like WHERE age = 69 vs. WHERE age = "69" previously hid model logic, capping Mistral-7B-v0.1 at 79.50% accuracy (adjusted to 82.60% post-formatting fixes). Implement a Logical Normalizer using Abstract Syntax Tree (AST) parsing to unify data types, standardize aliases, strip whitespace, and ignore hallucinations. This compares SQL logical structure, not text, yielding Mistral-7B-Instruct-v0.3's true 86.50% on a 1,000-sample stress test. v0.3's expanded context and structural improvements handle intent better, eliminating "punctuation taxes" for deterministic resilience in sovereign AI setups.

Apply this in production: parse generated and ground-truth SQL into ASTs, normalize (e.g., convert strings/numbers consistently), then validate equivalence. This reveals the model's reasoning depth, pushing local models toward 95% reliability without cloud dependency.

Target Three Failure Clusters to Hit 95% Reliability

Of the 13.5% remaining errors, fix these with schema-aware prompts and targeted fine-tuning:

  • Semantic Aggregation Bias (31% of errors): Model swaps MAX for SUM/AVG due to ambiguous math intent. Counter by injecting schema metadata emphasizing operation types (e.g., "metrics: age (numeric, aggregate with MAX)") in prompts.
  • 'How Many' Heuristic (28% of errors): Reflexive COUNT(*) on numerical columns when schema implies direct retrieval. Use "Schema DNA"—embed entity vs. metric distinctions—to guide inference.
  • Inference Silence (18% of errors): Empty outputs on complex multi-join/filter queries from attention dropout. Extend with chain-of-thought prompting or decompose queries into sub-steps.

These "smarter" failures signal semantic gaps, not syntax breaks, guiding next fine-tuning via QLoRA and Flash Attention 2 for high-stakes environments like SOMALA's H2E framework.

Deploy Fine-Tuned Model and Codebase Immediately

Run inference locally with the released Mistral-7B-v0.3-text-to-sql-flash-attention-2 weights, optimized for speed and context. Full pipeline—including training, Logical Normalizer, and 1,000-sample eval—is in a GitHub notebook using QLoRA. Test on your schema: load model, normalize outputs, benchmark logic accuracy to iterate toward production-grade Text-to-SQL without probabilistic fragility.