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Model Details
Model Description
SmolLM-360M-text2sql is a fine-tuned transformer model optimized for text-to-SQL generation tasks. The model takes natural language queries and converts them into SQL statements, enabling seamless database interactions through natural language. It was fine-tuned using the 🤗 Hugging Face Transformers library.
- Developed by: Aryann Tated
- Model type: Transformer-based model, fine-tuned for text-to-SQL translation
Uses
Direct Use
Convert natural language questions into SQL queries for querying databases.
Build user-friendly database query systems in applications such as business intelligence dashboards and customer support tools.
Downstream Use [optional]
- Integration into conversational agents for querying structured datasets.
- Automating SQL generation in educational or enterprise settings.
Out-of-Scope Use
- Not intended for querying highly sensitive or confidential databases without proper safeguards.
- Should not be used without verifying the output SQL queries for accuracy and security.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForCausalLM, AutoTokenizer
Load model and tokenizer
model_name = "aryanntated/SmolLM-360M-text2sql"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
Example usage
input_query = "How many employees were hired in 2023?"
inputs = tokenizer(input_query, return_tensors="pt")
outputs = model.generate(**inputs)
Decode and print the SQL query
generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_sql)
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