Update README.md

README.md

@@ -1,3 +1,83 @@
----
-license: apache-2.0
----
+---
+base_model: Qwen/Qwen2.5-Coder-3B-Instruct
+datasets: anonymous-2321/bird
+library_name: transformers
+tags:
+- generated_from_trainer
+- open-r1
+- Text2SQL
+- Reasoning
+licence: apache-2.0
+---
+
+# Model Information
+
+This model is the reasoning model for Text2SQL task introduced in [Think2SQL: Solving Text2SQL with Reasoning and Partial Scoring Rewards]()
+
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) on the [anonymous-2321/bird](https://huggingface.co/datasets/anonymous-2321/bird) dataset.
+It has been trained using [TRL](https://github.com/huggingface/trl). 
+
+
+
+## Quick start
+
+The best model performance are given with its System and User prompt.
+The model is intended to use with three input: question, evidence and the database schema. 
+
+
+Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
+
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
+
+```python
+import transformers
+import torch
+model_id = "anonymous-2321/Think2SQL-3B"
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+system_message = (
+    "You are a helpful AI Assistant that provides well-reasoned and detailed responses. "
+    "You first think about the reasoning process as an internal monologue and then provide the user with the answer. "
+    "Respond in the following format: <think>\n...\n</think>\n<answer>\n...\n</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.\n"
+    "Question:\n{question}\n\n"
+    "Evidence:\n{evidence}\n\n"
+    "Database Schema:\n{schema}\n\n"
+    "Return only the SQL script enclosed in <answer> tags."
+).strip()
+
+messages = [
+    {"role": "system", "content": system_message},
+    {"role": "user", "content": user_message},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=30_000,
+    temperature=0.7,
+    top_p=0.95
+)
+print(outputs[0]["generated_text"][-1])
+```
+
+## Training procedure
+
+This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
+
+### Framework versions
+
+- TRL: 0.17.0.dev0
+- Transformers: 4.51.0
+- Pytorch: 2.5.1
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1