OneSQL-v0.2-Qwen
Collection
Text-to-SQL Model
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5 items
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Updated
Your email will be used for anonymous survey. It will NOT be shared with anyone.
This model is the full-weight version of the adapter model OneSQL-v0.1-Qwen-3B.
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-3B"
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