---
license: mit
base_model: Deepseek-R1
tags:
  - text-generation
  - sql
  - lora
  - unsloth
  - Deepseek
---

# SQLNova - LoRA Fine-Tuned Deepseek 8B for Text-to-SQL Generation

**SQLNova** is a lightweight LoRA adapter fine-tuned on top of Unsloth’s Architecture. It is designed to convert natural language instructions into valid SQL queries with minimal compute overhead, making it ideal for integration into data-driven applications or chat interfaces.

The model was trained on over **100,000 natural language-to-SQL pairs** spanning diverse domains, including Education, Technical, Healthcare, and more.

---

## Model Dependencies

- **Python Version**: `3.10`
- **libraries**: `unsloth`
- pip install unsloth
  
## Model Highlights

- **Base model**: `Deepseek R1 8B Distilled Llama`
- **Tokenizer**: Compatible with `Deepseek R1 8B Distilled Llama`
- **Fine tuned for**: Text to SQL Converter
- **Accuracy**: > 85%
- **Language**: English Natural Language Sentences finetuned
- **Format**: `safetensors`

### General Information
- **Model type:** Text Generation
- **Language:** English  
- **License:** MIT
- **Base model:** DeepSeek R1 distilled on Llama3 8B
### Model Repository

- **Hugging Face Model Card:** [https://huggingface.co/mervp/SQLNova](https://huggingface.co/mervp/SQLNova)

---

## 💡 Intended Uses

### Applications

- Generating SQL queries from natural language prompts
- Powering AI assistants for databases
- Enhancing SQL query builders or no-code data tools
- Automating analytics workflows

---

## Limitations

While **SQLNova** performs well in many real-world scenarios Since its a Reasoning Model, there are some limitations:

- It may produce **invalid SQL** for rare or malformed inputs in rarest cases.
- Assumes a **generic SQL dialect**, resembling MySQL/PostgreSQL syntax.

### Recommendation for Use of Model

- Always **validate generated SQL** before executing in production.
- Include **schema context** in prompts to improve accuracy.
- Use with **human-in-the-loop** review for critical applications.


Thanks for visiting and downloading this model!
If this model helped you, please consider leaving a like. Your support helps this model reach more developers and encourages further improvements if any.
---

## How to Use the Model

```python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="mervp/SQLNova",
    max_seq_length=2048,
    dtype=None,
)

prompt = """ You are an text to SQL query translator.
             Users will ask you questions in English
             and you will generate a SQL query based on their question
             SQL has to be simple, The schema context has been provided to you.


### User Question:
{}

### Sql Context:
{}

### Sql Query:
{}
"""

question = "List the names of customers who have an account balance greater than 6000."
schema = """
CREATE TABLE socially_responsible_lending (
    customer_id INT,
    name VARCHAR(50),
    account_balance DECIMAL(10, 2)
);

INSERT INTO socially_responsible_lending VALUES
    (1, 'james Chad', 5000),
    (2, 'Jane Rajesh', 7000),
    (3, 'Alia Kapoor', 6000),
    (4, 'Fatima Patil', 8000);
"""

inputs = tokenizer(
    [prompt.format(question, schema, "")],
    return_tensors="pt",
    padding=True,
    truncation=True
).to("cuda")

output = model.generate(
    **inputs,
    max_new_tokens=256, 
    temperature=0.2,  
    top_p=0.9,          
    top_k=50,        
    do_sample=True     
)

decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)

if "### Sql Query:" in decoded_output:
    sql_query = decoded_output.split("### Sql Query:")[-1].strip()
else:
    sql_query = decoded_output.strip()

print(sql_query)