README.md

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license: other
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![Aquila_logo](./log.jpeg)

<h4 align="center">
    <p>
        <b>English</b> |
        <a href="https://huggingface.co/BAAI/AquilaSQL-7B/blob/main/README_zh.md">简体中文</a>
    </p>
</h4>


Aquila Language Model is the first open source language model that supports both Chinese and English knowledge, commercial license agreements, and compliance with domestic data regulations.

- 🌟 **Supports open source commercial licenses**. The source code of the Aquila series models is based on the [Apache 2.0 agreement](https://www.apache.org/licenses/LICENSE-2.0), while the model weight is based on the [BAAI Aquila Model License Agreement](https://huggingface.co/BAAI/AquilaChat-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf). Users can use it for commercial purposes as long as they meet the licensing restrictions.

- ✍️ **Possesses Chinese and English knowledge**. The Aquila series model is trained from scratch on a high-quality corpus of Chinese and English languages, with Chinese corpora accounting for about 40%, ensuring that the model accumulates native Chinese world knowledge during the pre-training phase, rather than translated knowledge.

- 👮‍♀️ **Complies with domestic data regulations**. The Chinese corpora of the Aquila series models come from Intelligence Source's accumulated Chinese datasets over the years, including Chinese internet data from over 10,000 sources (more than 99% of which are domestic sources), as well as high-quality Chinese literature and book data supported by authoritative domestic organizations. We will continue to accumulate high-quality and diverse datasets and incorporate them into the subsequent training of the Aquila base models.

- 🎯 **Continuous improvements and open sourcing**. We will continue to improve training data, optimize training methods, and enhance model performance, cultivate a flourishing "model tree" on a better base model foundation, and continuously update open-source versions.

The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels, including the [FlagAI GitHub repository](https://github.com/FlagAI-Open/FlagAI/), [FlagAI's Zhihu account](https://www.zhihu.com/people/95-22-20-18) and [FlagAI's official technical communication group](https://github.com/FlagAI-Open/FlagAI/blob/master/wechat-qrcode.jpg).

| Model        | Model Type | Description                                                  | Status    | GPUs Used   |
| ------------ | ---------- | ------------------------------------------------------------ | --------- | ----------- |
| AquilaSQL-7B | chat model | text2sql model, cotinue traind from the AquilaCode-base model, AquilaSQL achieved sota on the cspider leadboard | published | Nvidia-A100 |



We will continue to release improved versions of Aquila model as open source.
(https://huggingface.co/BAAI/AquilaSQL-7B/blob/main/change_log.log).

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## Inference

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = torch.device("cuda")
model_info = "BAAI/AquilaSQL-7B"

tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_info, trust_remote_code=True, torch_dtype=torch.float16, device_map='auto')

model.eval()
model.to(device)
torch.manual_seed(123)

text = "有多个数据库表，信息如下：\n表名为cars_data，包含的属性为cars_data.horsepower,cars_data.accelerate,cars_data.mpg,cars_data.id,cars_data.year;表名为continents，包含的属性为continents.contid,continents.continent;表名为countries，包含的属性为countries.continent,countries.countryname,countries.countryid;表名为model_list，包含的属性为model_list.model,model_list.maker,model_list.modelid，它们之间的关系为 countries.continent = continents.contid\n请为下面的问题编写sql查询语句：\n加速度比马力最大的汽车更大的汽车有多少辆？ "

def generate_prompt(input: str):
    prompt = f"A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: {input}###Assistant:"
    return prompt

stop_tokens = ["###", "[UNK]", "</s>","<|endoftext|>"]

with torch.no_grad():

    _input = generate_prompt(text)
    tokens = tokenizer.encode_plus(_input, None, max_length=None)['input_ids']
    tokens = torch.tensor(tokens)[None,].to(device)
    out = model.generate(tokens, do_sample=False, max_length=1024, eos_token_id=100007,max_new_tokens=512,
                            bad_words_ids=[[tokenizer.encode(token)[0] for token in stop_tokens]])[0]
    out = tokenizer.decode(out.cpu().numpy().tolist())
    print(out)
```


## License

AquilaSQL-7B open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/AquilaChat-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf)




# Acknowledgements

This work is supported by the National Science and Technology Major Project (No. 2022ZD0116300).
本项目受新一代人工智能国家科技重大专项（No. 2022ZD0116300）支持。