Privoid-ai/Privoid-SQL-Qwen3.5-2B

Privoid SQL (Qwen3.5-2B)

A LoRA fine-tune of Qwen/Qwen3.5-2B (revision 15852e8c) by Privoid. Give it a SQLite schema and a question; it replies with one SQLite query. GGUF files are in Privoid-ai/Privoid-SQL-Qwen3.5-2B-GGUF.

Benchmark: Spider dev, execution accuracy

Model	Correct results	Query ran without error
Qwen3.5-2B	53.5% (553/1034)	82.1%
Privoid SQL	72.0% (744/1034)	93.7%

• Every question in the Spider dev set whose reference query runs, on the real SQLite databases. None of these databases were used in training.
• A query is correct when its result rows match the reference query's rows. Order counts only when the reference uses ORDER BY.
• Both models get the same prompt and greedy decoding, with thinking off.
• Fine-tuning fixed 254 questions that the base model got wrong and broke 63 that it got right.
• This check is simpler than the official Spider test-suite evaluation, so these numbers are not directly comparable with the Spider leaderboard.

Per-question results are in benchmark/results.json.

Prompt format

System prompt:

You are Privoid SQL. Given a SQLite database schema and a question, reply with one SQLite query that answers it, and nothing else.

User message:

Database schema:
<CREATE TABLE statements, optionally with a few example rows in /* */ comments>

Question: <question>

Use thinking off (enable_thinking=False). With llama.cpp: llama-server -m Privoid-SQL-Qwen3.5-2B-Q8_0.gguf --jinja, then send chat_template_kwargs: {"enable_thinking": false}.

Training

• LoRA r=16, alpha=32 on the linear-attention, attention and MLP layers, on one NVIDIA L4.
• 240 updates in 39.3 minutes, stopped by time limit. Training loss 0.589 to 0.152.
• Training pool: 6918 rows from Spider train (with schemas and two example rows per table) and 3150 from Gretel synthetic text-to-SQL, kept only if they run in SQLite. The time limit allowed 240 of 518 batches, so about 46% of this pool was seen, once.

Credits and licence

• Base model: Qwen/Qwen3.5-2B, Apache-2.0.
• Spider (Yu et al., EMNLP 2018), CC BY-SA 4.0. Databases from the pinned re-host HAL-9001/spider-databases.
• gretelai/synthetic_text_to_sql, Apache-2.0.
• Because the training data includes Spider, this model is released under CC BY-SA 4.0.

Limitations

• SQLite only, one query per answer. It has not been tested on other SQL dialects or on very large schemas.
• It can return queries that run but answer a slightly different question. Check results before relying on them, and run generated SQL with read-only database access.