Add comprehensive model card for CSC-SQL model

This PR adds a comprehensive model card for the CSC-SQL model, linking it to the paper [CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning](https://huggingface.co/papers/2505.13271) and its GitHub repository.

It also adds the appropriate `pipeline_tag` (`text-generation`), `library_name` (`transformers`), and `license` (`cc-by-nc-4.0`) to the metadata, improving discoverability on the Hugging Face Hub (e.g., at https://huggingface.co/models?pipeline_tag=text-generation). Additionally, relevant `tags` such as `text-to-sql`, `reinforcement-learning`, and `qwen` have been added to further categorize the model.

README.md

@@ -0,0 +1,110 @@
+---
+pipeline_tag: text-generation
+library_name: transformers
+license: cc-by-nc-4.0
+tags:
+- text-to-sql
+- reinforcement-learning
+- qwen
+---
+
+# CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning
+
+This repository contains the `CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct` model, a key component of the CSC-SQL framework, as presented in the paper [CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning](https://huggingface.co/papers/2505.13271).
+
+## Abstract
+Large language models (LLMs) have demonstrated strong capabilities in translating natural language questions about relational databases into SQL queries. In particular, test-time scaling techniques such as Self-Consistency and Self-Correction can enhance SQL generation accuracy by increasing computational effort during inference. However, these methods have notable limitations: Self-Consistency may select suboptimal outputs despite majority votes, while Self-Correction typically addresses only syntactic errors. To leverage the strengths of both approaches, we propose CSC-SQL, a novel method that integrates Self-Consistency and Self-Correction. CSC-SQL selects the two most frequently occurring outputs from parallel sampling and feeds them into a merge revision model for correction. Additionally, we employ the Group Relative Policy Optimization (GRPO) algorithm to fine-tune both the SQL generation and revision models via reinforcement learning, significantly enhancing output quality. Experimental results confirm the effectiveness and generalizability of CSC-SQL. On the BIRD private test set, our 7B model achieves 71.72% execution accuracy, while the 32B model achieves 73.67%.
+
+For more details, refer to the [paper](https://huggingface.co/papers/2505.13271) and the [official GitHub repository](https://github.com/CycloneBoy/csc_sql).
+
+## Framework Overview
+![CSC-SQL Framework](https://raw.githubusercontent.com/CycloneBoy/csc_sql/main/data/image/csc_sql_framework.png)
+
+## Code
+The official code repository for CSC-SQL is available on GitHub: [https://github.com/CycloneBoy/csc_sql](https://github.com/CycloneBoy/csc_sql)
+
+## Main Results
+Performance comparison of different Text-to-SQL methods on BIRD dev and test dataset:
+![CSC-SQL Results](https://raw.githubusercontent.com/CycloneBoy/csc_sql/main/data/image/csc_sql_result_main.png)
+<img src="https://raw.githubusercontent.com/CycloneBoy/csc_sql/main/data/image/csc_sql_result_main.png" height="500" alt="csc_sql_result main">
+
+## Model Checkpoints
+This model is part of a collection of checkpoints related to CSC-SQL, also available on Hugging Face:
+
+| **Model**                     | HuggingFace                                                                                |
+|-------------------------------|--------------------------------------------------------------------------------------------|
+| CscSQL-Merge-Qwen2.5-Coder-3B-Instruct | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-3B-Instruct) |
+| CscSQL-Merge-Qwen2.5-Coder-7B-Instruct | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct) |
+| CscSQL-Grpo-Qwen2.5-Coder-3B-Instruct | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-3B-Instruct)  |
+| CscSQL-Grpo-XiYanSQL-QwenCoder-3B-2502 | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-XiYanSQL-QwenCoder-3B-2502) |
+| CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct)  |
+| CscSQL-Grpo-XiYanSQL-QwenCoder-7B-2502 | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-XiYanSQL-QwenCoder-7B-2502) |
+
+## Usage
+You can load this model using the `transformers` library. Here's a basic example for inference:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+
+model_name = "cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct"
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16, # Or torch.float16 depending on your hardware
+    device_map="auto"
+)
+model.eval()
+
+# Example prompt for text-to-SQL generation
+# Note: The prompt format might need to align with the model's specific training
+# and database schema format for optimal text-to-SQL performance.
+prompt = "Translate the following question to SQL: 'What are the names of all employees?'"
+
+# Encode the prompt
+input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
+
+# Set generation configuration based on the model's generation_config.json
+generation_config = GenerationConfig(
+    bos_token_id=tokenizer.bos_token_id,
+    eos_token_id=[tokenizer.eos_token_id, 151643], # Include <|endoftext|> as eos_token_id
+    pad_token_id=tokenizer.bos_token_id, # Or use tokenizer.pad_token_id if different
+    temperature=0.7,
+    max_new_tokens=512,
+    do_sample=True,
+    top_p=0.8,
+    repetition_penalty=1.1,
+    top_k=20,
+)
+
+# Generate SQL query
+output_ids = model.generate(
+    input_ids,
+    generation_config=generation_config
+)
+
+# Decode the generated SQL
+generated_sql = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print(generated_sql)
+
+# For detailed usage, including how to integrate with the full CSC-SQL framework
+# for improved accuracy via reinforcement learning, please refer to the
+# official GitHub repository: https://github.com/CycloneBoy/csc_sql
+```
+
+## Citation
+If you find this work helpful or inspiring, please feel free to cite it:
+
+```bibtex
+@misc{sheng2025cscsqlcorrectiveselfconsistencytexttosql,
+      title={CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning}, 
+      author={Lei Sheng and Shuai-Shuai Xu},
+      year={2025},
+      eprint={2505.13271},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.13271}, 
+}
+```