How to Build a Fine-Tuned Model for English-to-SQL Tasks Using Gemma

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There’s been a quiet shift in the AI world. While most developers still focus on GPT-4 and Claude, more are turning to open-source models like Gemma—especially for structured, domain-specific tasks such as converting natural language into SQL queries.

I recently built a full English-to-SQL pipeline using Hugging Face’s AutoTrain, and instead of relying on GPT APIs, I fine-tuned Gemma, Google DeepMind’s lightweight open-source LLM. Here’s why—and how you can do it too.

What Is Gemma?

Gemma is Google’s open-source large language model, available in 2B and 7B parameter sizes. The instruction-tuned versions (gemma-2b-it, gemma-7b-it) are well-suited for tasks like summarization, translation—and yes, text-to-SQL generation.

Unlike GPT-3.5 or GPT-4, Gemma is fully open. That means you can fine-tune it, host it locally or on your own cloud infrastructure, and customize it for your exact schema and use case.

Why Not Just Use GPT?

OpenAI’s models are powerful, but they have limitations:

  • You can’t fine-tune them on your own schema
  • Every query costs money
  • You’re locked into the API
  • You can’t inspect or debug the model

For lightweight experiments, that’s fine. But if you’re building an internal tool or assistant that generates SQL daily, these restrictions become costly—both in performance and budget.

Why Gemma Is a Better Fit

1. Full Access and Control

Gemma’s open weights let you train, adapt, inspect, and debug your own version. You can align it with your schema, tune it to your team’s language, and ensure complete data privacy.

2. Cost Efficiency

With GPT, every call adds up. With Gemma, once you’ve trained your model, you can run it for free as many times as needed—locally or on Hugging Face.

3. Seamless Hugging Face Support

Gemma works out of the box with Hugging Face Transformers, AutoTrain, TGI, PEFT, and LoRA adapters. You can go from dataset to deployable model in hours.

4. Structured Output Accuracy

Gemma performs well on structured tasks. Unlike some LLMs that hallucinate or ramble, Gemma—when fine-tuned on datasets like WikiSQL or Spider—produces reliable, syntax-correct queries like:

sqlCopyEditSELECT name FROM users WHERE signup_year = 2023;

5. Real-World Use Cases

Imagine building a tool that lets business users ask:

How many new users signed up in June?

With Gemma, you can train a model that understands your schema and responds with valid SQL—tailored to your tables and naming conventions.

How I Built the Model

Here’s the exact pipeline I followed:

  1. Started with a dataset of question–SQL pairs (WikiSQL format)
  2. Converted it into a CSV with input and output columns
  3. Used Hugging Face AutoTrain (Text2Text task) to fine-tune gemma-2b-it
  4. Prefixed inputs with Translate English to SQL:
  5. Deployed the model using Hugging Face’s Inference Endpoints

It took only a few hours to go from data to a functioning SQL-generating model.

What Is AutoTrain?

AutoTrain is Hugging Face’s no-code fine-tuning platform. You upload your data (in CSV or JSON), choose your model, set a few training parameters, and it handles everything—training, evaluation, checkpoints, and deployment.

AutoTrain supports:

  • Text2Text tasks (like SQL generation)
  • Text classification
  • Multi-label classification
  • Token classification
  • Embeddings

You can train a working LLM without writing a single line of code. For developers, it saves time. For non-experts, it lowers the barrier to fine-tuning powerful models.

What Is LoRA and Quantization?

As LLMs grow in size, fine-tuning them becomes expensive. Two techniques help:

LoRA (Low-Rank Adaptation)

LoRA freezes the base model and adds a few trainable layers (adapters). Instead of updating all billions of parameters, it only adjusts a small subset.

Benefits:

  • Saves GPU memory
  • Trains faster
  • Keeps the original model intact
  • Easy to reuse or stack adapters

Quantization

Quantization reduces model size by using lower precision (e.g., 8-bit or 4-bit instead of 32-bit float). This means you can run models like Gemma on standard hardware.

Benefits:

  • Smaller model files
  • Lower memory usage
  • Faster inference
  • Makes local deployment practical

Together, LoRA and quantization let you train and deploy powerful models without high compute costs.

Final Thoughts

Gemma may not have the name recognition of GPT-4, but it offers something even more valuable for developers and AI builders: freedom, flexibility, and affordability.

For structured tasks like English-to-SQL generation, where accuracy, schema control, and repeatability matter, Gemma is an excellent choice. And with Hugging Face AutoTrain, you can go from idea to model in a day.

If you’re working on database assistants, internal tools, or just want to experiment with LLMs and real-world data, give Gemma a try. It works—and it doesn’t come with a meter running.

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