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junmingg/
qwen2.5-coder-7b-text2sql-filtered

Name: qwen2.5-coder-7b-text2sql-filtered
Rating: 2.5 (10 reviews)

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Qwen2.5-Coder-7B Text-to-SQL — filtered-train variant (QLoRA)

Same recipe as junmingg/qwen2.5-coder-7b-text2sql, but the 80 training rows whose gold SQL is unparseable (0.32% — WikiSQL label noise) were dropped before fine-tuning (24,920 train rows). This is the label-noise ablation described in the main model card.

Results (held-out 500 examples — identical references)

Model	Exact match	Semantic equiv.	SQL validity
Base (zero-shot)	3.8%	67.0%	100.0%
Main model (full 25k train)	78.8%	86.2%	99.2%
This model (filtered 24.9k train)	78.6%	85.4%	99.8%

On the valid-reference subset (497 rows) this model reaches 100% validity. Cleaning the noisy labels moved validity, not accuracy — exact-match and semantic-equivalence are unchanged within run-to-run noise.

Use the main model unless you specifically want this ablation. Usage, training details, evaluation methodology, and limitations are identical to the main model card. Code + harness: https://github.com/junmingg/Unsloth-Qwen2.5-Coder-7b-Text-to-SQL-SFT

License & attribution

Apache-2.0 (base model). Training data: b-mc2/sql-create-context (CC-BY-4.0).

junmingg/qwen2.5-coder-7b-text2sql-filtered

Qwen2.5-Coder-7B Text-to-SQL — filtered-train variant (QLoRA)

Results (held-out 500 examples — identical references)

License & attribution

junmingg/
qwen2.5-coder-7b-text2sql-filtered