---
title: OmniSQL-14B
canonical_url: "https://www.modelscope.cn/models/seeklhy/OmniSQL-14B"
md_url: "https://www.modelscope.cn/models/seeklhy/OmniSQL-14B.md"
repository: seeklhy/OmniSQL-14B
last_updated: 2025-03-03
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
parameters: 14.8B
tensor_type:
  - BF16
library_name:
  - safetensors
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 37977
stars: 7
---

# OmniSQL-14B

> OmniSQL-14B - seeklhy 在 ModelScope 开源的模型。OmniSQL - Synthesizing High-quality Text-to-SQL Data at Scale

seeklhy/OmniSQL-14B 是 ModelScope 魔搭社区上的 14.8B 参数机器学习模型，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: seeklhy/OmniSQL-14B
- **Parameters**: 14.8B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 37977
- **Stars**: 7
- **Last updated**: 2025-03-03

Source: https://www.modelscope.cn/models/seeklhy/OmniSQL-14B

---

# OmniSQL - Synthesizing High-quality Text-to-SQL Data at Scale

## Introduction
We introduce the first million-scale text-to-SQL dataset, **SynSQL-2.5M**, containing over **2.5 million diverse and high-quality data samples**, spanning more than **16,000 databases from various domains**.

Building on SynSQL-2.5M, we introduce **OmniSQL**, a family of powerful text-to-SQL models available in three sizes: **7B, 14B, and 32B**. During the fine-tuning process, we also integrate training sets from Spider and BIRD, which provide high-quality, human-labeled data.

For more details, please refer to our GitHub repo: [RUCKBReasoning/OmniSQL](https://github.com/RUCKBReasoning/OmniSQL).

## Quickstart with vLLM and Transformers
Here are some sample code snippets to quickly use OmniSQL for performing text-to-SQL.

### Prompt Template
The prompt template used by OmniSQL is defined as follows:
````python
input_prompt_template = '''Task Overview:
You are a data science expert. Below, you are provided with a database schema and a natural language question. Your task is to understand the schema and generate a valid SQL query to answer the question.

Database Engine:
SQLite

Database Schema:
{db_details}
This schema describes the database's structure, including tables, columns, primary keys, foreign keys, and any relevant relationships or constraints.

Question:
{question}

Instructions:
- Make sure you only output the information that is asked in the question. If the question asks for a specific column, make sure to only include that column in the SELECT clause, nothing more.
- The generated query should return all of the information asked in the question without any missing or extra information.
- Before generating the final SQL query, please think through the steps of how to write the query.

Output Format:
In your answer, please enclose the generated SQL query in a code block:
```
-- Your SQL query
```

Take a deep breath and think step by step to find the correct SQL query.'''
````

Replace the placeholders for "db_details" and "question" to get started. Note that "db_details" is formatted as `CREATE TABLE` statements (i.e., DDL) of tables in the database. You can add database values and column descriptions in DDLs with SQL comments. External knowledge can be concatenated with the natural language question and placed in the "question" placeholder. OmniSQL currently supports only SQLite, as the SQL queries in SynSQL-2.5M are synthesized using the SQLite dialect.

We provide example prompts in the `examples` folder.

### Inference with vLLM
The code snippet below shows how to use OmniSQL with vLLM.
```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

prompt = input_prompt_template.format(db_details = "...", question = "...")
model_path = "seeklhy/OmniSQL-7B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
sampling_params = SamplingParams(
    temperature = 0, 
    max_tokens = 2048,
    n = 1
)

llm = LLM(
    model = model_path,
    dtype = "float16", 
    tensor_parallel_size = 1,
    max_model_len = 8192,
    gpu_memory_utilization = 0.92,
    swap_space = 8,
    enforce_eager = True,
    disable_custom_all_reduce = True,
    trust_remote_code = True
)

chat_prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt = True, tokenize = False
)

outputs = llm.generate([chat_prompt], sampling_params)

for output in outputs:
    responses = [o.text for o in output.outputs]
    print(responses[0])
```
Ensure you have correctly installed [vLLM](https://docs.vllm.ai/en/latest/) in your environment.

### Inference with Transformers
Optionally, you can use Transformers for inference.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

prompt = input_prompt_template.format(db_details = "...", question = "...")
model_path = "seeklhy/OmniSQL-7B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16
).to("cuda:0")

chat_prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt = True, tokenize = False
)

inputs = tokenizer([chat_prompt], return_tensors="pt")
inputs = inputs.to(model.device)

output_ids = model.generate(
    **inputs,
    eos_token_id = tokenizer.eos_token_id,
    max_new_tokens = 2048
)

input_len = len(inputs.input_ids[0])
output_ids = output_ids[0][input_len:]

response = tokenizer.batch_decode([output_ids], skip_special_tokens = True)[0]
print(response)
```

## Limitations
SynSQL-2.5M is an English dataset focused on the SQLite database engine, so its performance in multi-language and multi-SQL dialect scenarios may be limited. However, you can synthesize new data samples using our proposed framework to suit your scenarios. After synthesizing a new dataset, you can use OmniSQL for further fine-tuning, as it is a strong starting point for text-to-SQL capabilities.

## Contact
If you have any questions, we encourage you to either create Github issues or get in touch with Haoyang Li at lihaoyang.cs@ruc.edu.cn.