Improve model card: Update paper link, add GitHub link and usage example
Browse filesThis PR improves the model card by:
1. Updating the paper link to the official Hugging Face Papers page: [SLM-SQL: An Exploration of Small Language Models for Text-to-SQL](https://huggingface.co/papers/2507.22478).
2. Adding a prominent link to the GitHub repository in the "Important Links" section.
3. Including a "How to use" section with a Python code snippet that demonstrates how to generate SQL from a natural language question using the `transformers` library, including a database schema for better context.
These enhancements will make the model more accessible and easier for the community to understand and use.
README.md
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---
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pipeline_tag: text-generation
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library_name: transformers
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license: cc-by-nc-4.0
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tags:
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- text-to-sql
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- reinforcement-learning
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---
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# SLM-SQL: An Exploration of Small Language Models for Text-to-SQL
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### Important Links
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📖[
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🤖[ModelScope](https://modelscope.cn/collections/SLM-SQL-624bb6a60e9643) |
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## News
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<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_framework.png" height="500" alt="slmsql_framework">
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### Main Results
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<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_bird_result.png" height="500" alt="slm_sql_result">
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| **Model** | Base Model | Train Method | Modelscope | HuggingFace |
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|------------------------------------------|------------------------------|--------------|---------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|
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| SLM-SQL-Base-0.5B | Qwen2.5-Coder-0.5B-Instruct | SFT | [
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| SLM-SQL-0.5B | Qwen2.5-Coder-0.5B-Instruct | SFT + GRPO | [
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| CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct | Qwen2.5-Coder-0.5B-Instruct | SFT + GRPO | [
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| SLM-SQL-Base-1.5B | Qwen2.5-Coder-1.5B-Instruct | SFT | [
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| SLM-SQL-1.5B | Qwen2.5-Coder-1.5B-Instruct | SFT + GRPO | [
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| CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct | Qwen2.5-Coder-1.5B-Instruct | SFT + GRPO | [
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| SLM-SQL-Base-0.6B | Qwen3-0.6B | SFT | [
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| SLM-SQL-0.6B | Qwen3-0.6B | SFT + GRPO | [
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| SLM-SQL-Base-1.3B | deepseek-coder-1.3b-instruct | SFT | [
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| SLM-SQL-1.3B | deepseek-coder-1.3b-instruct | SFT + GRPO | [
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| SLM-SQL-Base-1B | Llama-3.2-1B-Instruct | SFT | [
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## Dataset
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| **Dataset**
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| SynsQL-Think-916k
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| SynsQL-Merge-Think-310k
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| bird train and dev dataset | [
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## TODO
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---
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library_name: transformers
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license: cc-by-nc-4.0
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pipeline_tag: text-generation
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tags:
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- text-to-sql
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- reinforcement-learning
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---
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# SLM-SQL: An Exploration of Small Language Models for Text-to-SQL
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### Important Links
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📖[Paper](https://huggingface.co/papers/2507.22478) |
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\ud83d\udcbb[GitHub Repository](https://github.com/CycloneBoy/slm_sql) |
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🤗[HuggingFace Collection](https://huggingface.co/collections/cycloneboy/slm-sql-688b02f99f958d7a417658dc) |
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🤖[ModelScope](https://modelscope.cn/collections/SLM-SQL-624bb6a60e9643) |
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## News
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<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_framework.png" height="500" alt="slmsql_framework">
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## How to use
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You can use the model with the `transformers` library for Text-to-SQL tasks. Make sure you have `transformers` and `torch` installed.
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "cycloneboy/SLM-SQL-0.5B" # Or any other SLM-SQL model from the collection
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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# Example for Text-to-SQL
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db_schema = """
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CREATE TABLE Employee (
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employee_id INTEGER PRIMARY KEY,
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name TEXT,
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department TEXT,
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salary INTEGER
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);
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CREATE TABLE Department (
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department_id INTEGER PRIMARY KEY,
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name TEXT,
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location TEXT
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);
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"""
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question = "What are the names of employees in the 'Sales' department earning more than 50000?"
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prompt = f"Given the database schema:
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{db_schema}
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Translate the following question to SQL: {question}"
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messages = [
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{"role": "system", "content": "You are a helpful assistant that translates natural language questions into SQL queries."},
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{"role": "user", "content": prompt}
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]
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input_ids = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=256,
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do_sample=True,
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temperature=0.7,
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top_k=50,
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top_p=0.95
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)
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response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
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print(response)
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# Expected output similar to: SELECT name FROM Employee WHERE department = 'Sales' AND salary > 50000
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```
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### Main Results
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<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_bird_result.png" height="500" alt="slm_sql_result">
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| **Model** | Base Model | Train Method | Modelscope | HuggingFace |
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|------------------------------------------|------------------------------|--------------|---------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|
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| SLM-SQL-Base-0.5B | Qwen2.5-Coder-0.5B-Instruct | SFT | [\ud83e\udd16 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-0.5B) | [\ud83e\udd17 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-0.5B) |
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| SLM-SQL-0.5B | Qwen2.5-Coder-0.5B-Instruct | SFT + GRPO | [\ud83e\udd16 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-0.5B) | [\ud83e\udd17 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-0.5B) |
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| CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct | Qwen2.5-Coder-0.5B-Instruct | SFT + GRPO | [\ud83e\udd16 Modelscope](https://modelscope.cn/models/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct) | [\ud83e\udd17 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct) |
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| SLM-SQL-Base-1.5B | Qwen2.5-Coder-1.5B-Instruct | SFT | [\ud83e\udd16 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-1.5B) | [\ud83e\udd17 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-1.5B) |
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| SLM-SQL-1.5B | Qwen2.5-Coder-1.5B-Instruct | SFT + GRPO | [\ud83e\udd16 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-1.5B) | [\ud83e\udd17 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-1.5B) |
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| CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct | Qwen2.5-Coder-1.5B-Instruct | SFT + GRPO | [\ud83e\udd16 Modelscope](https://modelscope.cn/models/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct) | [\ud83e\udd17 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct) |
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| SLM-SQL-Base-0.6B | Qwen3-0.6B | SFT | [\ud83e\udd16 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-0.6B) | [\ud83e\udd17 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-0.6B) |
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| SLM-SQL-0.6B | Qwen3-0.6B | SFT + GRPO | [\ud83e\udd16 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-0.6B) | [\ud83e\udd17 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-0.6B) |
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| SLM-SQL-Base-1.3B | deepseek-coder-1.3b-instruct | SFT | [\ud83e\udd16 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-1.3B ) | [\ud83e\udd17 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-1.3B ) |
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| SLM-SQL-1.3B | deepseek-coder-1.3b-instruct | SFT + GRPO | [\ud83e\udd16 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-1.3B ) | [\ud83e\udd17 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-1.3B ) |
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| SLM-SQL-Base-1B | Llama-3.2-1B-Instruct | SFT | [\ud83e\udd16 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-1B ) | [\ud83e\udd17 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-1B ) |
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## Dataset
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| **Dataset** | Modelscope | HuggingFace |
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|---|---|---|
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| SynsQL-Think-916k | [\ud83e\udd16 Modelscope](https://modelscope.cn/datasets/cycloneboy/SynsQL-Think-916k) | [\ud83e\udd17 HuggingFace](https://huggingface.co/datasets/cycloneboy/SynsQL-Think-916k) |
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| SynsQL-Merge-Think-310k | [\ud83e\udd16 Modelscope](https://modelscope.cn/datasets/cycloneboy/SynsQL-Merge-Think-310k) | [\ud83e\udd17 HuggingFace](https://huggingface.co/datasets/cycloneboy/SynsQL-Merge-Think-310k) |
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| bird train and dev dataset | [\ud83e\udd16 Modelscope](https://modelscope.cn/datasets/cycloneboy/bird_train) | [\ud83e\udd17 HuggingFace](https://huggingface.co/datasets/cycloneboy/bird_train) |
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## TODO
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