CoolFace
Modelpublic

lanfers/gaussdb-sql-expert-7b

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face
0likes30downloads
Model Card

GaussDB SQL Expert 7B

[中文版 README](README_zh.md)

A domain-specific database assistant fine-tuned on Qwen2.5-Coder-7B-Instruct, specialized in SQL generation, optimization, cross-database migration, error diagnosis, and more.

Model Overview

ItemDetails
Base ModelQwen/Qwen2.5-Coder-7B-Instruct
Parameters7.6B (Dense)
Fine-tuningLoRA (rank=64, alpha=128, target=all linear layers)
Trainable Params161M (2.08% of total)
Training Data29,863 ShareGPT conversations + 1,571 validation
Hardware1x NVIDIA H100 80GB
Training Time3.5 hours
FrameworkLLaMA-Factory v0.9.4
PrecisionBF16

Core Capabilities

  • —Text2SQL: Natural language to SQL with support for window functions, recursive CTEs, MERGE, subqueries, and more
  • —SQL Tuning: Index invalidation analysis, execution plan interpretation, parameter optimization advice
  • —SQL Migration: Oracle / MySQL / SQL Server → GaussDB syntax conversion (50+ difference points)
  • —Error Diagnosis: Deadlock, WAL bloat, connection exhaustion, OOM, and 20+ common production issues
  • —SQL Explanation: Logic breakdown and readability analysis of complex queries
  • —Boundary Safety: Dangerous operation interception, clarification requests, out-of-scope rejection

Supports 9 major databases: GaussDB, Oracle, MySQL, PostgreSQL, SQL Server, PolarDB, DM (Dameng), KingBase, Sybase

Benchmark Results

Evaluated on 100 automated test cases (20 per category) using keyword matching:

CategoryScoreNotes
Text2SQL20/20 (100%)Window functions, CTE, MERGE, pagination all correct
SQL Tuning18/20 (90%)Index invalidation, implicit conversion, parameter tuning
SQL Migration20/20 (100%)Oracle/MySQL/SQL Server → GaussDB conversion
Error Diagnosis20/20 (100%)Deadlock, WAL, OOM, connection exhaustion
Boundary Safety16/20 (80%)Dangerous operation alerts, out-of-scope rejection
Overall94/100 (94%)

Quick Start

Requirements

  • —Python >= 3.9
  • —PyTorch >= 2.0
  • —GPU with >= 16GB VRAM (recommended) or CPU (slower)
  • —~15GB disk space for model weights

Installation

bash
# 1. Install dependencies
pip install torch transformers accelerate

# 2. (Optional) Install Flash Attention 2 for faster inference on NVIDIA GPUs
pip install flash-attn --no-build-isolation

Download Model

The model will be downloaded automatically on first use via from_pretrained(). You can also download it manually:

bash
# Option A: Using huggingface-cli
pip install huggingface_hub
huggingface-cli download lanfers/gaussdb-sql-expert-7b --local-dir ./gaussdb-sql-expert-7b

# Option B: Using git-lfs
git lfs install
git clone https://huggingface.co/lanfers/gaussdb-sql-expert-7b

# Option C: Using Python
python -c "
from huggingface_hub import snapshot_download
snapshot_download('lanfers/gaussdb-sql-expert-7b', local_dir='./gaussdb-sql-expert-7b')
"
China Mainland Users: If download is slow, use a mirror: ``bash HF_ENDPOINT=https://hf-mirror.com huggingface-cli download lanfers/gaussdb-sql-expert-7b --local-dir ./gaussdb-sql-expert-7b ``

Inference

python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "lanfers/gaussdb-sql-expert-7b"
device = "cuda"  # or "mps" (Mac) / "cpu"

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map=device,
    trust_remote_code=True,
)
model.eval()

messages = [
    {"role": "system", "content": "You are a professional database expert assistant, proficient in Oracle, MySQL, PostgreSQL, GaussDB, PolarDB, DM, KingBase, Sybase, SQL Server."},
    {"role": "user", "content": "Database: GaussDB 505\n\nFind the top 3 highest-paid employees in each department, showing department name, employee name, salary, and rank."},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=1024,
        temperature=0.7,
        do_sample=True,
        top_p=0.8,
        repetition_penalty=1.05,
    )

response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)

Example Outputs

Input: Find the top 3 highest-paid employees in each department

Output:

sql
SELECT dept_name, emp_name, salary, rk
FROM (
    SELECT d.dept_name, e.emp_name, e.salary,
        DENSE_RANK() OVER(PARTITION BY e.dept_id ORDER BY e.salary DESC) AS rk
    FROM employees e
    JOIN departments d ON e.dept_id = d.dept_id
) t
WHERE rk <= 3;

Input: Migrate Oracle to GaussDB: SELECT NVL(name, 'unknown') FROM users WHERE ROWNUM <= 10

Output:

sql
SELECT COALESCE(name, 'unknown') FROM users LIMIT 10;
-- NVL → COALESCE, ROWNUM → LIMIT

Training Details

Hyperparameters

yaml
# LoRA config
lora_rank: 64
lora_alpha: 128
lora_dropout: 0.05
lora_target: all  # q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

# Training config
learning_rate: 2.0e-5
lr_scheduler_type: cosine
warmup_ratio: 0.1
num_train_epochs: 3
per_device_train_batch_size: 8
gradient_accumulation_steps: 4  # effective batch_size = 32
cutoff_len: 2048
optim: adamw_torch
bf16: true
gradient_checkpointing: true

Training Loss

Total steps: 2,799 | Duration: 3h 29m

Step    Epoch   Train Loss   Eval Loss
  200   0.21      1.217       1.216
  600   0.64      1.038       1.104
 1000   1.07      1.035       1.076
 1400   1.50      1.062       1.058
 1800   1.93      1.062       1.045
 2200   2.36      0.966       1.044
 2600   2.79      0.959       1.042  ← best checkpoint

Final trainloss=1.039, evalloss=1.042. Near-identical values indicate no overfitting.

Training Data Distribution

CategoryProportionDescription
Text2SQL~30%Natural language → SQL generation
SQL Tuning~20%Slow query analysis, index optimization
SQL Migration~15%Cross-database syntax conversion
Error Diagnosis~15%Production incident troubleshooting
Operations~10%Parameter tuning, backup & recovery
Boundary Safety~10%Dangerous operation alerts, scope rejection

Limitations

  • —Boundary safety has room for improvement: may execute DELETE without WHERE or DROP DATABASE without warning
  • —Limited coverage of GaussDB 505 advanced features (e.g., column-store tables, distributed features)
  • —Text-only input; does not support images (e.g., execution plan screenshots)
  • —Recommended to add inference-side safety rules for production environments

Citation

If this model is helpful, please cite:

bibtex
@misc{gaussdb-sql-expert-7b,
  title={GaussDB SQL Expert 7B},
  author={lanfers},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/lanfers/gaussdb-sql-expert-7b}
}

License

Fine-tuned from Qwen2.5-Coder-7B-Instruct under the Apache 2.0 License.