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pipeline_tag: text-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
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) |