---
title: NL2SQL-StarCoder-15B
canonical_url: "https://www.modelscope.cn/models/iic/NL2SQL-StarCoder-15B"
md_url: "https://www.modelscope.cn/models/iic/NL2SQL-StarCoder-15B.md"
repository: iic/NL2SQL-StarCoder-15B
last_updated: 2024-01-29
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - gpt_bigcode
architectures:
  - GPTBigCodeForCausalLM
library_name:
  - pytorch
frameworks:
  - Pytorch
inference_backends:
  - "deploy_task text/emb"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 4955
stars: 18
---

# NL2SQL-StarCoder-15B

> NL2SQL-StarCoder-15B - iic 在 ModelScope 开源的模型。NL2SQL-StarCoder-15B 是在基础模型 StarCoder 上通过 QLoRA 对自然语言生成SQL任务进行微调的 15B Code-LLM。

iic/NL2SQL-StarCoder-15B 是 ModelScope 魔搭社区上的text-generation模型，采用 other 许可，可用 deploy_task text/emb、sglang 0.5.2、vllm 0.9.2 部署。

- **Repository**: iic/NL2SQL-StarCoder-15B
- **License**: other
- **Tasks**: text-generation
- **Inference backends**: deploy_task text/emb, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 4955
- **Stars**: 18
- **Last updated**: 2024-01-29

Source: https://www.modelscope.cn/models/iic/NL2SQL-StarCoder-15B

---

# Model Card for NL2SQL-StarCoder-15B

## 模型介绍

NL2SQL-StarCoder-15B 是在基础模型 StarCoder 上通过 QLoRA 对自然语言生成SQL任务进行微调的 15B Code-LLM。 

## Requirements

- python>=3.8
- pytorch>=2.0.0
- transformers==4.32.0
- CUDA 11.4
## 推理数据格式

推理数据为模型在训练数据格式下拼接的字符串形式，它也是推理时输入prompt拼接的方式：
```python
"""
<|user|>
/* Given the following database schema: */
CREATE TABLE "table_name" (
"col1" int,
...
...
)

/* Write a sql to answer the following question: {问题} */
<|assistant|>
```sql
{输出SQL}
```<|end|>
"""
```

## 快速开始

```python
import torch
from modelscope import snapshot_download, AutoModelForCausalLM, AutoTokenizer,GenerationConfig
model_dir = snapshot_download("iic/NL2SQL-StarCoder-15B")
tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", 
                              trust_remote_code=True, torch_dtype=torch.float16)
tokenizer.padding_side = "left"
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids("<fim_pad>")
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("<|endoftext|>")
tokenizer.pad_token = "<fim_pad>"
tokenizer.eos_token = "<|endoftext|>"

model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", 
                              trust_remote_code=True, torch_dtype=torch.float16)
model.eval()

text = '<|user|>\n/* Given the following database schema: */\nCREATE TABLE "singer" (\n"Singer_ID" int,\n"Name" text,\n"Country" text,\n"Song_Name" text,\n"Song_release_year" text,\n"Age" int,\n"Is_male" bool,\nPRIMARY KEY ("Singer_ID")\n)\n\n/* Write a sql to answer the following question: Show countries where a singer above age 40 and a singer below 30 are from. */<|end|>\n'
inputs = tokenizer(text, return_tensors='pt', padding=True, add_special_tokens=False).to("cuda")
outputs = model.generate(
        inputs=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        max_new_tokens=512,
        top_p=0.95,
        temperature=0.1,
        do_sample=False,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id
    )
gen_text = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(gen_text)
```