Instructions to use samadpls/querypls-prompt2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use samadpls/querypls-prompt2sql with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="samadpls/querypls-prompt2sql")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("samadpls/querypls-prompt2sql")
model = AutoModelForCausalLM.from_pretrained("samadpls/querypls-prompt2sql", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use samadpls/querypls-prompt2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "samadpls/querypls-prompt2sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "samadpls/querypls-prompt2sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/samadpls/querypls-prompt2sql

SGLang

How to use samadpls/querypls-prompt2sql with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "samadpls/querypls-prompt2sql" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "samadpls/querypls-prompt2sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "samadpls/querypls-prompt2sql" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "samadpls/querypls-prompt2sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use samadpls/querypls-prompt2sql with Docker Model Runner:
```
docker model run hf.co/samadpls/querypls-prompt2sql
```

querypls-prompt2sql / README.md

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	---
	pipeline_tag: text-generation
	---


	<img src='/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F648dd721b91c3ead953a5ae0%2FzUj6oxW4WHXQjFHYhTduY.png%26%23x27%3B%3C%2Fspan%3E 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)