Instructions to use onekq-ai/OneSQL-v0.2-Qwen-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use onekq-ai/OneSQL-v0.2-Qwen-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="onekq-ai/OneSQL-v0.2-Qwen-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("onekq-ai/OneSQL-v0.2-Qwen-1.5B")
model = AutoModelForCausalLM.from_pretrained("onekq-ai/OneSQL-v0.2-Qwen-1.5B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use onekq-ai/OneSQL-v0.2-Qwen-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "onekq-ai/OneSQL-v0.2-Qwen-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onekq-ai/OneSQL-v0.2-Qwen-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/onekq-ai/OneSQL-v0.2-Qwen-1.5B

SGLang

How to use onekq-ai/OneSQL-v0.2-Qwen-1.5B 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 "onekq-ai/OneSQL-v0.2-Qwen-1.5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onekq-ai/OneSQL-v0.2-Qwen-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "onekq-ai/OneSQL-v0.2-Qwen-1.5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onekq-ai/OneSQL-v0.2-Qwen-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use onekq-ai/OneSQL-v0.2-Qwen-1.5B with Docker Model Runner:
```
docker model run hf.co/onekq-ai/OneSQL-v0.2-Qwen-1.5B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Disclaimer

Your email will be used for anonymous survey. It will NOT be shared with anyone.

Introduction

This model is the full-weight version of the adapter model OneSQL-v0.1-Qwen-1.5B.

Quick start

To use this model, craft your prompt to start with your database schema in the form of CREATE TABLE, followed by your natural language query preceded by --. Make sure your prompt ends with SELECT in order for the model to finish the query for you.

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel

model_name = "onekq-ai/OneSQL-v0.2-Qwen-1.5B"
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.padding_side = "left"

generator = pipeline("text-generation", model=model, tokenizer=tokenizer, return_full_text=False)

prompt = """
CREATE TABLE students (
    id INTEGER PRIMARY KEY,
    name TEXT,
    age INTEGER,
    grade TEXT
);

-- Find the three youngest students
SELECT """

result = generator(f"<|im_start|>system\nYou are a SQL expert. Return code only.<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n")[0]
print(result["generated_text"])

The model response is the finished SQL query without SELECT

* FROM students ORDER BY age ASC LIMIT 3

Downloads last month: 11

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for onekq-ai/OneSQL-v0.2-Qwen-1.5B

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Quantized

unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit

Finetuned

(26)

this model

Quantizations

2 models

Collection including onekq-ai/OneSQL-v0.2-Qwen-1.5B

OneSQL-v0.2-Qwen

Collection

Text-to-SQL Model • 5 items • Updated Aug 2, 2025