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Model: lanfers/gaussdb-sql-expert-7b Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- zh
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- en
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base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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tags:
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- sql
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- text2sql
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- database
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- gaussdb
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- lora
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- fine-tuned
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pipeline_tag: text-generation
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library_name: transformers
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datasets:
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- custom
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model-index:
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- name: GaussDB-SQL-Expert-7B
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results:
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- task:
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type: text-generation
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name: Database SQL Expert
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metrics:
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- name: Text2SQL Accuracy
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type: accuracy
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value: 100
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- name: SQL Migration Accuracy
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type: accuracy
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value: 100
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- name: Error Diagnosis Accuracy
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type: accuracy
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value: 100
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- name: SQL Tuning Accuracy
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type: accuracy
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value: 90
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- name: Boundary Safety Accuracy
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type: accuracy
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value: 80
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- name: Overall Accuracy
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type: accuracy
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value: 94
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---
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# GaussDB SQL Expert 7B
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**[中文版 README](README_zh.md)**
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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.
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## Model Overview
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| Item | Details |
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|------|---------|
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| Base Model | [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) |
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| Parameters | 7.6B (Dense) |
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| Fine-tuning | LoRA (rank=64, alpha=128, target=all linear layers) |
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| Trainable Params | 161M (2.08% of total) |
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| Training Data | 29,863 ShareGPT conversations + 1,571 validation |
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| Hardware | 1x NVIDIA H100 80GB |
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| Training Time | 3.5 hours |
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| Framework | [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) v0.9.4 |
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| Precision | BF16 |
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## Core Capabilities
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- **Text2SQL**: Natural language to SQL with support for window functions, recursive CTEs, MERGE, subqueries, and more
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- **SQL Tuning**: Index invalidation analysis, execution plan interpretation, parameter optimization advice
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- **SQL Migration**: Oracle / MySQL / SQL Server → GaussDB syntax conversion (50+ difference points)
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- **Error Diagnosis**: Deadlock, WAL bloat, connection exhaustion, OOM, and 20+ common production issues
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- **SQL Explanation**: Logic breakdown and readability analysis of complex queries
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- **Boundary Safety**: Dangerous operation interception, clarification requests, out-of-scope rejection
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**Supports 9 major databases**: GaussDB, Oracle, MySQL, PostgreSQL, SQL Server, PolarDB, DM (Dameng), KingBase, Sybase
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## Benchmark Results
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Evaluated on 100 automated test cases (20 per category) using keyword matching:
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| Category | Score | Notes |
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|----------|-------|-------|
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| Text2SQL | 20/20 (100%) | Window functions, CTE, MERGE, pagination all correct |
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| SQL Tuning | 18/20 (90%) | Index invalidation, implicit conversion, parameter tuning |
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| SQL Migration | 20/20 (100%) | Oracle/MySQL/SQL Server → GaussDB conversion |
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| Error Diagnosis | 20/20 (100%) | Deadlock, WAL, OOM, connection exhaustion |
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| Boundary Safety | 16/20 (80%) | Dangerous operation alerts, out-of-scope rejection |
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| **Overall** | **94/100 (94%)** | |
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## Quick Start
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### Requirements
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- Python >= 3.9
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- PyTorch >= 2.0
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- GPU with >= 16GB VRAM (recommended) or CPU (slower)
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- ~15GB disk space for model weights
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### Installation
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```bash
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# 1. Install dependencies
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pip install torch transformers accelerate
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# 2. (Optional) Install Flash Attention 2 for faster inference on NVIDIA GPUs
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pip install flash-attn --no-build-isolation
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```
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### Download Model
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The model will be downloaded automatically on first use via `from_pretrained()`. You can also download it manually:
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```bash
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# Option A: Using huggingface-cli
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pip install huggingface_hub
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huggingface-cli download lanfers/gaussdb-sql-expert-7b --local-dir ./gaussdb-sql-expert-7b
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# Option B: Using git-lfs
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git lfs install
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git clone https://huggingface.co/lanfers/gaussdb-sql-expert-7b
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# Option C: Using Python
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python -c "
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from huggingface_hub import snapshot_download
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snapshot_download('lanfers/gaussdb-sql-expert-7b', local_dir='./gaussdb-sql-expert-7b')
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"
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```
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> **China Mainland Users**: If download is slow, use a mirror:
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> ```bash
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> HF_ENDPOINT=https://hf-mirror.com huggingface-cli download lanfers/gaussdb-sql-expert-7b --local-dir ./gaussdb-sql-expert-7b
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> ```
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### Inference
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_path = "lanfers/gaussdb-sql-expert-7b"
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device = "cuda" # or "mps" (Mac) / "cpu"
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype=torch.float16,
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device_map=device,
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trust_remote_code=True,
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)
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model.eval()
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messages = [
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{"role": "system", "content": "You are a professional database expert assistant, proficient in Oracle, MySQL, PostgreSQL, GaussDB, PolarDB, DM, KingBase, Sybase, SQL Server."},
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{"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."},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=1024,
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temperature=0.7,
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do_sample=True,
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top_p=0.8,
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repetition_penalty=1.05,
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)
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response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
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print(response)
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```
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### Example Outputs
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**Input**: Find the top 3 highest-paid employees in each department
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**Output**:
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```sql
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SELECT dept_name, emp_name, salary, rk
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FROM (
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SELECT d.dept_name, e.emp_name, e.salary,
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DENSE_RANK() OVER(PARTITION BY e.dept_id ORDER BY e.salary DESC) AS rk
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FROM employees e
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JOIN departments d ON e.dept_id = d.dept_id
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) t
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WHERE rk <= 3;
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```
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**Input**: Migrate Oracle to GaussDB: `SELECT NVL(name, 'unknown') FROM users WHERE ROWNUM <= 10`
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**Output**:
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```sql
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SELECT COALESCE(name, 'unknown') FROM users LIMIT 10;
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-- NVL → COALESCE, ROWNUM → LIMIT
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```
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## Training Details
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### Hyperparameters
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```yaml
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# LoRA config
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lora_rank: 64
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lora_alpha: 128
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lora_dropout: 0.05
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lora_target: all # q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
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# Training config
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learning_rate: 2.0e-5
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lr_scheduler_type: cosine
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warmup_ratio: 0.1
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num_train_epochs: 3
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per_device_train_batch_size: 8
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gradient_accumulation_steps: 4 # effective batch_size = 32
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cutoff_len: 2048
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optim: adamw_torch
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bf16: true
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gradient_checkpointing: true
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```
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### Training Loss
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```
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Total steps: 2,799 | Duration: 3h 29m
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Step Epoch Train Loss Eval Loss
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200 0.21 1.217 1.216
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600 0.64 1.038 1.104
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1000 1.07 1.035 1.076
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1400 1.50 1.062 1.058
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1800 1.93 1.062 1.045
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2200 2.36 0.966 1.044
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2600 2.79 0.959 1.042 ← best checkpoint
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```
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Final train_loss=1.039, eval_loss=1.042. Near-identical values indicate no overfitting.
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### Training Data Distribution
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| Category | Proportion | Description |
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|----------|-----------|-------------|
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| Text2SQL | ~30% | Natural language → SQL generation |
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| SQL Tuning | ~20% | Slow query analysis, index optimization |
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| SQL Migration | ~15% | Cross-database syntax conversion |
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| Error Diagnosis | ~15% | Production incident troubleshooting |
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| Operations | ~10% | Parameter tuning, backup & recovery |
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| Boundary Safety | ~10% | Dangerous operation alerts, scope rejection |
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## Limitations
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- Boundary safety has room for improvement: may execute `DELETE` without `WHERE` or `DROP DATABASE` without warning
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- Limited coverage of GaussDB 505 advanced features (e.g., column-store tables, distributed features)
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- Text-only input; does not support images (e.g., execution plan screenshots)
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- Recommended to add inference-side safety rules for production environments
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## Citation
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If this model is helpful, please cite:
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```bibtex
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@misc{gaussdb-sql-expert-7b,
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title={GaussDB SQL Expert 7B},
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author={lanfers},
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year={2026},
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publisher={HuggingFace},
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url={https://huggingface.co/lanfers/gaussdb-sql-expert-7b}
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}
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```
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## License
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Fine-tuned from [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) under the Apache 2.0 License.
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