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--- |
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base_model: Qwen/Qwen3-4B |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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- open-r1 |
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- Text2SQL |
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- Reasoning |
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licence: apache-2.0 |
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language: |
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- en |
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--- |
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# Model Information |
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This model is the reasoning model for the Text-to-SQL task introduced in [Think2SQL: Blueprinting Reward Density and Advantage Scaling for Effective Text-to-SQL Reasoning]() |
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This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) with thinking disabled on the [BIRD](https://bird-bench.github.io/) dataset. |
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It has been trained using [TRL](https://github.com/huggingface/trl). |
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## Quick start |
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The best model performance is given with its System and User prompts. |
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The model is intended to be used with three inputs: question, evidence, and the database schema. |
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Required `transformers > 4.51.0` to have Qwen3. Make sure to update your transformers installation via `pip install --upgrade transformers`. |
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```python |
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import transformers |
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import torch |
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model_id = "anonymous-2321/Think2SQL-4B" |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model_id, |
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model_kwargs={"torch_dtype": torch.bfloat16}, |
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device_map="auto", |
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) |
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system_message =""" |
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You are a data science expert that provides well-reasoned and detailed responses. Your task is to understand the schema and generate a valid SQL query to answer the question. |
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You first think about the reasoning process as an internal monologue and then provide the user with the answer. |
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Respond in the following format: |
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<reasoning> |
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... |
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</reasoning> |
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<answer> |
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... |
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</answer> |
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""".strip() |
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user_message = """ |
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Answer the following question with the SQL code. Use the piece of evidence and base your answer on the database schema. |
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Given the question, the evidence and the database schema, return in the <answer> tags only the SQL script that addresses the question. |
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Database Engine: |
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SQLite |
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Question: |
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Return the product name, sorted alphabetically and by price in descending order. |
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Evidence: |
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Database Schema: |
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CREATE TABLE products ( |
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id INTEGER PRIMARY KEY, |
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name TEXT NOT NULL, |
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price REAL NOT NULL |
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); |
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CREATE TABLE customers ( |
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id INTEGER PRIMARY KEY, |
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name TEXT NOT NULL, |
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email TEXT NOT NULL |
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); |
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""" |
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messages = [ |
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{"role": "system", "content": system_message}, |
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{"role": "user", "content": user_message}, |
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] |
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outputs = pipeline( |
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messages, |
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max_new_tokens=4096, |
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temperature=0.6, |
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top_p=0.95, |
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top_k=20 |
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) |
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print(outputs[0]["generated_text"][-1]) |
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``` |
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## 📖 Overview |
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Think2SQL is a systematic study on injecting reasoning capabilities into Text-to-SQL through Reinforcement Learning with Verifiable Rewards (RLVR). We uncover the critical interplay between reward density, advantage scaling, and model capacity, proposing novel execution-guided dense rewards and optimal scaling strategies. Our 4B-parameter model achieves reasoning capabilities competitive with state-of-the-art models, while providing a comprehensive analysis for optimizing Text-to-SQL reasoning under computational constraints. |
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**Key Contributions:** |
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- Execution-guided dense reward function that outperforms binary signals |
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- Analysis of advantage scaling mechanics for models of different sizes |
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- Evaluation of cold start effects and supervised fine-tuning impact |
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- Pareto frontier mapping for training efficiency optimization |
