Create README.md

README.md

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+---
+base_model: Qwen/Qwen3-8B
+library_name: transformers
+tags:
+- generated_from_trainer
+- open-r1
+- Text2SQL
+- Reasoning
+licence: apache-2.0
+language:
+- en
+---
+
+# Model Information
+
+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]()
+
+
+This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) with thinking disabled on the [BIRD](https://bird-bench.github.io/) dataset.
+It has been trained using [TRL](https://github.com/huggingface/trl). 
+
+
+
+## Quick start
+
+The best model performance is given with its System and User prompts.
+The model is intended to be used with three inputs: question, evidence, and the database schema. 
+
+
+Required `transformers > 4.51.0` to have Qwen3. Make sure to update your transformers installation via `pip install --upgrade transformers`.
+
+```python
+import transformers
+import torch
+model_id = "anonymous-2321/Think2SQL-8B"
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+system_message ="""
+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.
+You first think about the reasoning process as an internal monologue and then provide the user with the answer.
+Respond in the following format:
+<reasoning>
+    ...
+</reasoning>
+<answer>
+    ...
+</answer>
+""".strip()
+
+user_message = """
+Answer the following question with the SQL code. Use the piece of evidence and base your answer on the database schema.
+Given the question, the evidence and the database schema, return in the <answer> tags only the SQL script that addresses the question.
+
+Database Engine:
+SQLite
+
+Question:
+Return the product name, sorted alphabetically and by price in descending order.
+
+
+Evidence:
+
+
+Database Schema:
+CREATE TABLE products (
+    id INTEGER PRIMARY KEY,
+    name TEXT NOT NULL,
+    price REAL NOT NULL
+);
+
+CREATE TABLE customers (
+    id INTEGER PRIMARY KEY,
+    name TEXT NOT NULL,
+    email TEXT NOT NULL
+);
+"""
+
+
+messages = [
+    {"role": "system", "content": system_message},
+    {"role": "user", "content": user_message},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=4096,
+    temperature=0.6,
+    top_p=0.95,
+    top_k=20
+)
+print(outputs[0]["generated_text"][-1])
+```
+
+## 📖 Overview
+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.
+
+**Key Contributions:**
+- Execution-guided dense reward function that outperforms binary signals
+- Analysis of advantage scaling mechanics for models of different sizes
+- Evaluation of cold start effects and supervised fine-tuning impact
+- Pareto frontier mapping for training efficiency optimization