You can deploy this model and start earning money today!

pavan-naik/
Llama-3.2-1B-Instruct-Text-to-SQL

Name: Llama-3.2-1B-Instruct-Text-to-SQL
Availability: InStock

transformers

safetensors

b-mc2/sql-create-context

meta-llama/Llama-3.2-1B-Instruct

finetune

endpoints_compatible

Views

No views yet

Text-to-SQL Model Usage

Model Details

Base Model: meta-llama/Llama-3.2-1B-Instruct
Fine-tuned Model: pavan-naik/Llama-3.2-1B-Instruct-Text-to-SQL
Task: Text to SQL Query Generation
Framework: PyTorch with 🤗 Transformers and PEFT

Installation

pip install peft transformers bitsandbytes

Required Imports

python

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
import torch

Loading the Model

1. Configure Quantization (Optional)

python

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)

2. Load Base Model and Tokenizer

python

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.2-1B-Instruct",
    #quantization_config=bnb_config, #uncomment if you want to use quatized version. 
    device_map="auto"
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    "pavan-naik/Llama-3.2-1B-Instruct-Text-to-SQL",
    trust_remote_code=True
)
tokenizer.pad_token = tokenizer.eos_token

3. Load PEFT Adapter

model = PeftModel.from_pretrained(base_model, "pavan-naik/Llama-3.2-1B-Instruct-Text-to-SQL")

Generating SQL Queries

Prompt Template

python

sql_prompt_template = """You are a database management system expert, proficient in Structured Query Language (SQL).
Your job is to write an SQL query that answers the following question, based on the given database schema and any additional information provided.  Use SQLite syntax.
Please output only SQL (without any explanations).
### Question: {question}
### Schema: {context}
### Completion: """

Generation Function

python

def generate_sql(question, context, model, tokenizer, max_length=128):
    prompt = sql_prompt_template.format(question=question, context=context)
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=max_length)
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    
    prompt_length = len(inputs["input_ids"][0])
    outputs = model.generate(
        **inputs,
        max_length=prompt_length + max_length,
        num_return_sequences=1,
        temperature=0.7,
        do_sample=True,
    )
    
    sql_answer = tokenizer.decode(outputs[0][prompt_length:], skip_special_tokens=True).strip()
    return sql_answer

Example Usage

python

# Define your question and database schema
question = "For each continent, show the city with the highest population and what percentage of its country's total population it represents"
context = """
CREATE TABLE city (city_id INTEGER, name VARCHAR, population INTEGER, country_id INTEGER);
CREATE TABLE country (country_id INTEGER, name VARCHAR, continent VARCHAR)
"""

# Generate SQL query
sql_query = generate_sql(question, context, model, tokenizer)
print(sql_query)

Notes

The model uses SQLite syntax
Adjust max_length parameter based on your query complexity
Temperature can be modified to control randomness in generation (0.0 for deterministic output)
The model performs best with clear schema definitions and well-structured questions

Requirements

Python 3.7+
PyTorch
Transformers
PEFT (Parameter-Efficient Fine-Tuning)
bitsandbytes (for quantization)