Model Card for Falcon SQL Generator (LoRA Fine-Tuned)
A lightweight Falcon-1B model fine-tuned using LoRA on Spider-style SQL generation examples. The model takes in a user query and schema context and generates corresponding SQL queries. It supports few-shot prompting and can be integrated with retrieval-based systems.
Model Details
Model Description
This model is a fine-tuned version of tiiuae/falcon-rw-1b using Parameter-Efficient Fine-Tuning (LoRA) for the text-to-SQL task. It is trained on custom Spider-style examples that map natural language questions to valid SQL queries over a provided schema context.
- Developed by: revanth kumar
- Finetuned by: revanth kumar
- Model type: Causal Language Model (
AutoModelForCausalLM) - Language(s): English (natural language input) and SQL (structured query output)
- License: Apache 2.0 (inherits from base Falcon model)
- Finetuned from:
tiiuae/falcon-rw-1b
Model Sources
Uses
Direct Use
This model can be directly used for natural language to SQL generation. It supports queries like:
"List all employees earning more than 50000"
→SELECT * FROM employees WHERE salary > 50000;
It is suitable for:
- Low-code/no-code query interfaces
- Data analyst assistant tools
- SQL tutoring bots
Downstream Use
This model can be integrated into retrieval-augmented systems, or further fine-tuned on enterprise-specific schema and query examples.
Out-of-Scope Use
- It may not generalize well to highly complex, nested, or ambiguous queries.
- Should not be used in production environments involving sensitive financial or health data without further validation.
Bias, Risks, and Limitations
- The model may generate incorrect or suboptimal SQL if the prompt is ambiguous or the schema context is incomplete.
- It inherits any limitations or biases from the Falcon base model.
Recommendations
Users should validate generated queries before executing them on production databases. Schema consistency and prompt clarity are key to good performance.
How to Get Started with the Model
You can load and run the model using the Hugging Face transformers library:
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("revanthkumarg/falcon-sql-lora")
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-rw-1b")
prompt = "List all employees earning more than 50000:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))