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Qwen-2.5-3b-Text_to_SQL

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+Here’s a polished version of the `README.md` tailored for your fine-tuned model:
+---
+```markdown
 ---
 library_name: transformers
+license: mit
+datasets:
+- gretelai/synthetic_text_to_sql
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: text-generation
 ---
+# Fine-Tuned LLM for Text-to-SQL Conversion
+This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) designed to convert natural language queries into SQL statements. It was trained on the `gretelai/synthetic_text_to_sql` dataset and can provide both SQL queries and table schema context when needed.
+---
 ## Model Details
 ### Model Description
+This model has been fine-tuned to help users generate SQL queries based on natural language prompts. In scenarios where table schema context is missing, the model is trained to generate schema definitions along with the SQL query, making it a robust solution for various Text-to-SQL tasks.
+- **Base Model:** [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
+- **Dataset:** [Gretel AI Synthetic Text-to-SQL Dataset](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
+- **Language:** English
+- **License:** MIT
+### Key Features
+1. **Text-to-SQL Conversion:** Converts natural language queries into accurate SQL statements.
+2. **Schema Generation:** Generates table schema context when none is provided.
+3. **Optimized for Analytics and Reporting:** Handles SQL queries with aggregation, grouping, and filtering.
+---
+## Usage
 ### Direct Use
+To use the model for text-to-SQL conversion, you can load it using the `transformers` library as shown below:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("your-model-id")
+model = AutoModelForCausalLM.from_pretrained("your-model-id")
+# Input prompt
+query = "What is the total number of hospital beds in each state?"
+# Tokenize input and generate output
+inputs = tokenizer(query, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=512)
+# Decode and print
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+### Example Output
+Input:
+`What is the total number of hospital beds in each state?`
+Output:
+```sql
+Context:
+CREATE TABLE Beds (State VARCHAR(50), Beds INT);
+INSERT INTO Beds (State, Beds) VALUES ('California', 100000), ('Texas', 85000), ('New York', 70000);
+SQL Query:
+SELECT State, SUM(Beds) FROM Beds GROUP BY State;
+```
+---
 ## Training Details
+### Dataset
+The model was fine-tuned on the `gretelai/synthetic_text_to_sql` dataset, which includes diverse natural language queries mapped to SQL queries, with optional schema contexts.
+## Limitations
+1. **Complex Queries:** May struggle with highly nested or advanced SQL tasks.
+2. **Non-English Prompts:** Optimized for English only.
+3. **Context Dependence:** May generate incorrect schemas without explicit instructions.