| --- |
| language: en |
| tags: |
| - sql |
| - code-generation |
| - reinforcement-learning |
| - text-generation |
| datasets: |
| - spider |
|
|
| --- |
| |
| # Model Card for RL-GRPO-SQL-Model |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model type**: Fine-tuned Causal Language Model with Reinforcement Learning |
| - **Training approach**: Reinforcement Learning with GRPO (Group Relative Policy Optimization) |
| - **Task**: SQL generation and understanding |
| - **Developed by**: Ali Assi |
|
|
| ## Training Data |
|
|
| - **Data sources**: Spider train set |
| - **Preprocessing**: Parsing and validation |
| - **Languages**: English |
|
|
| ## How to Use |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| tokenizer = AutoTokenizer.from_pretrained("ALI-USER/rl-grpo-sql-model") |
| model = AutoModelForCausalLM.from_pretrained("ALI-USER/rl-grpo-sql-model") |
| |
| # Example usage |
| prompt = "Generate SQL for: Find all customers with orders over $100" |
| inputs = tokenizer(prompt, return_tensors="pt") |
| outputs = model.generate(**inputs, max_length=512) |
| print(tokenizer.decode(outputs[0])) |
| ``` |
|
|
| ## Limitations |
|
|
| - Model performance may vary depending on database schema complexity |
|
|
| ## Ethical Considerations |
|
|
| - May generate SQL queries that are inefficient or unsafe if not properly validated |
| - Should be used with query validation before execution |
|
|
| ## Intended Uses |
|
|
| **Primary use cases:** |
| - Natural language to SQL translation |
| - SQL code generation assistance |
| - Educational purposes for SQL understanding |
|
|
| **Out-of-scope uses:** |
| - Direct production deployment without query validation |
| - Non-English language queries (not trained for this) |
