A schema-grounded text-to-SQL model: given a database schema and a plain-English question, it returns a single SQLite query. LoRA fine-tune of Qwen/Qwen2.5-Coder-7B-Instruct, trained and quantized on Apple Silicon with MLX, on a mix of Spider and BIRD.

This model is the artifact of an honest investigation, not a cherry-picked demo — see Results and "What I learned".

Results (execution accuracy)

Evaluated by execution accuracy: run the predicted and gold SQL against the real SQLite database and compare result sets. Dev splits the model never saw in training.

Metric	Base (7B)	Querysmith	Δ
Spider exec-acc (n=180)	61.7%	67.2%	+5.5
BIRD exec-acc (n=200)	55.0%	53.0%	−2.0
Phrasing consistency (n=40)	62.0%	63.5%	+1.5

The fine-tune gives a solid +5.5 on Spider, is flat/slightly down on the much harder BIRD, and shows no meaningful phrasing-robustness gain.

What I learned (the honest part)

This project started from a real observation: Databricks Genie (NL→SQL) works well in its UI but is brittle to question phrasing when driven via API. The hypothesis was that a fine-tuned, phrasing-robust model would close that gap.

The robustness eval did not support that hypothesis — and the reason why is the real finding: Genie's brittleness is a property of its retrieval / example-grounding layer, not of the underlying LLM's phrasing sensitivity. A strong base model (Qwen2.5-Coder-7B) is already robust to surface paraphrases, so fine-tuning had little robustness headroom to capture. The right lever for the Genie-API problem is better example-grounding / retrieval — not fine-tuning the model.

The model itself is still a useful, honestly-benchmarked schema-grounded text-to-SQL fine-tune with a real Spider gain.

Usage

python

from mlx_lm import load, generate
model, tok = load("ajayk007/Qwen2.5-Coder-7B-Querysmith")
SYSTEM = ("You are a text-to-SQL generator. Given a database schema and a question, "
          "reply with a single valid SQLite query. Output only the SQL on one line.")
schema = "CREATE TABLE employees(id, name, dept_id, salary);\nCREATE TABLE departments(id, name);"
question = "average salary per department name"
user = f"Schema:\n{schema}\n\nQuestion: {question}"
prompt = tok.apply_chat_template(
    [{"role": "system", "content": SYSTEM}, {"role": "user", "content": user}],
    add_generation_prompt=True, tokenize=False)
print(generate(model, tok, prompt=prompt, max_tokens=192, verbose=False))

For BIRD-style questions you can add a \n\nHint: <external knowledge> line before the question, matching the training format.

Training

Method: LoRA (rank 16, 8 layers), MLX, 1200 iters, batch 2, seq 1536. Val loss 1.52 → 0.137.
Data: ~13.7k schema-grounded (schema + question → SQL) pairs from Spider + BIRD. See ajayk007/querysmith-spider-bird.
Eval: execution accuracy on Spider + BIRD dev; methodology in the GitHub repo.

Limitations & safety

SQLite dialect; schema must be provided in the prompt (no built-in retrieval).
BIRD-hard questions needing external knowledge remain weak.
Review generated SQL before running it against any real database.

Part of a series of focused "English → developer DSL" fine-tunes:

Qwen2.5-Coder-1.5B-Shellsmith — English → shell command.

License

Apache-2.0 (model weights, inheriting from the base). Training data derives from Spider and BIRD (CC BY-SA 4.0) — see the dataset card.

ajayk007/Qwen2.5-Coder-7B-Querysmith

Qwen2.5-Coder-7B-Querysmith