ap-sql-v1

A LoRA adapter for Qwen/Qwen2.5-Coder-7B-Instruct that turns Accounts Payable questions into read-only Oracle SQL. It is built for an AI data-analyst assistant that answers questions over a schema modelled on Oracle Fusion AP, Payments and Supplier tables.

The model gets a system prompt containing the relevant table DDL (from schema retrieval) and a business glossary. It answers with a single ```sql block. It has also learned to fix a query when it is given the Oracle error from its previous attempt, and to answer follow-up questions in a conversation.

Results

Execution accuracy on the 179-case held-out test split. The generated SQL and the gold SQL were both run on Oracle 23ai and their result sets compared. A case counts only when the rows match exactly. The adapter was served by vLLM 0.12.0 on the AWQ base Qwen/Qwen2.5-Coder-7B-Instruct-AWQ.

Model	T1	T2	T3	T4	T5	Overall
Base Qwen2.5-Coder-7B-Instruct-AWQ	57%	32%	21%	32%	56%	35%
ap-sql-v1	97%	96%	90%	89%	89%	93%

Invented-column errors (ORA-00904) fell from 43 cases to 0. The adapter adds about 1 second of median latency (2.8 s to 3.8 s on an RTX 5070 Ti Laptop GPU).

Caveats. 149 of the 179 test cases are templated, as is most of the training set. No test case shares a template group or an identical question with the training data, but real user questions will vary more than the test set. The eval uses each case's stored prompt, so it measures the model on its own; schema retrieval is outside its scope.

Usage

vLLM

vllm serve Qwen/Qwen2.5-Coder-7B-Instruct-AWQ --quantization awq_marlin \
  --enable-lora --max-lora-rank 16 --lora-modules ap-sql-v1=samrat-kar/ap-sql-v1

Then send model="ap-sql-v1" to the OpenAI-compatible /v1/chat/completions endpoint.

Transformers + PEFT

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct", torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(base, "samrat-kar/ap-sql-v1")
tok = AutoTokenizer.from_pretrained("samrat-kar/ap-sql-v1")

Use the same system-prompt format as the training data (see the dataset). The schema must be given as CREATE TABLE DDL.

Training

Method	QLoRA: 4-bit NF4 base with double quantisation, bf16 compute
LoRA	r=16, alpha=32, dropout=0.05; q/k/v/o/gate/up/down projections
Data	1,441 train / 95 val rows from samrat-kar/ap-sql-peft
Schedule	3 epochs, 273 optimizer steps, lr 2e-4 cosine, effective batch 16, paged AdamW 8-bit
Max length	3,840 tokens
Final train loss	0.0157
Compute	2.5 h, peak 17.5 GB GPU memory

The loss is computed on the assistant turn only.

Limitations

• Trained on a single demo schema. Other schemas, or other Fusion modules, need new training data.
• Produces Oracle dialect only (FETCH FIRST N ROWS ONLY, ADD_MONTHS, TRUNC).
• Always run the output through read-only guardrails and a read-only database user. The model is not a security boundary.