README.md

---
license: apache-2.0
datasets:
- samadpls/querypls-prompt2sql-dataset
- b-mc2/sql-create-context
tags:
- stabilityai/StableBeluga-7B
- langchain
- opensource
- stabilityai
- SatbleBeluga-7B
language:
- en



pipeline_tag: text2text-generation
---


<img src='https://cdn-uploads.huggingface.co/production/uploads/648dd721b91c3ead953a5ae0/zUj6oxW4WHXQjFHYhTduY.png' align='center'>

# 🛢💬 Querypls-Prompt2SQL



## Overview

Querypls-Prompt2SQL is a 💬 text-to-SQL generation model developed by [samadpls](https://github.com/samadpls). It is designed for generating SQL queries based on user prompts.

## Model Usage

To get started with the model in Python, you can use the following code:

```python
from transformers import pipeline, AutoTokenizer

question = "how to get all employees from table0"
prompt = f'Your task is to create SQL query of the following {question}, just SQL query and no text'

tokenizer = AutoTokenizer.from_pretrained("samadpls/querypls-prompt2sql")
pipe = pipeline(task='text-generation', model="samadpls/querypls-prompt2sql", tokenizer=tokenizer, max_length=200)

result = pipe(prompt)
print(result[0]['generated_text'])
```

Adjust the `question` variable with the desired question, and the generated SQL query will be printed.

## Training Details

The model was trained on Google Colab, and its purpose is to be used in the [Querypls](https://github.com/samadpls/Querypls) project with the following training and validation loss progression:

```yaml
Copy code
Step     Training Loss    Validation Loss
943      2.332100         2.652054
1886     2.895300         2.551685
2829     2.427800         2.498556
3772     2.019600         2.472013
4715     3.391200         2.465390
```
`However, note that the model may be too large to load in certain environments.`

For more information and details, please refer to the provided [documentation](https://huggingface.co/stabilityai/StableBeluga-7B).


## Model Card Authors

- 🤖 [samadpls](https://github.com/samadpls)