| | --- |
| | base_model: unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit |
| | library_name: transformers |
| | model_name: onekq-ai/OneSQL-v0.2-Qwen-3B |
| | tags: |
| | - generated_from_trainer |
| | - unsloth |
| | - trl |
| | - sft |
| | licence: apache-2.0 |
| | pipeline_tag: text-generation |
| | --- |
| | |
| | # 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-3B](https://huggingface.co/onekq-ai/OneSQL-v0.1-Qwen-3B). |
| |
|
| | # 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. |
| |
|
| | ```python |
| | 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** |
| | ```sql |
| | * FROM students ORDER BY age ASC LIMIT 3 |
| | ``` |