| --- |
| base_model: Qwen/Qwen2.5-Coder-7B-Instruct |
| library_name: peft |
| license: apache-2.0 |
| pipeline_tag: text-generation |
| language: [en] |
| datasets: [samrat-kar/ap-sql-peft] |
| tags: |
| - base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct |
| - lora |
| - qlora |
| - text-to-sql |
| - oracle |
| - accounts-payable |
| --- |
| |
| # 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 |
|
|
| ```bash |
| 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 |
|
|
| ```python |
| 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. |
|
|
