---
base_model: meta-llama/Llama-3.2-3B
library_name: transformers
pipeline_tag: text-generation
tags:
  - text-to-sql
  - sql
  - llama
  - llama-3.2
  - qlora
  - lora
  - unsloth
  - gguf
language:
  - en
datasets:
  - gretelai/synthetic_text_to_sql
---

# Llama 3.2 3B — Text-to-SQL

A fine-tuned version of **Meta Llama 3.2 3B** for converting natural-language questions into SQL queries.

The model was fine-tuned using **QLoRA** with a 4-bit quantized base model and LoRA adapters. After training, the adapter was merged with the base model to produce this standalone model.

## Model Repositories

### Merged Model

The base model and trained LoRA adapter have been merged into a standalone model:

https://huggingface.co/farehaheha/llama3.2-3B-text-to-sql

### Quantized GGUF Model

A `Q4_K_M` GGUF quantized version is available for efficient local inference with llama.cpp and other GGUF-compatible runtimes:

https://huggingface.co/farehaheha/llama3.2-3B-text-to-sql-Q4_K_M-GGUF

## Training Details

| Parameter | Value |
|---|---|
| Base Model | `meta-llama/Llama-3.2-3B` |
| Fine-tuning Method | QLoRA |
| Base Model Quantization During Training | 4-bit |
| LoRA Rank | 16 |
| Training Dataset | `gretelai/synthetic_text_to_sql` |
| Dataset Split Used | Test split |
| Training Samples | ~5.85K |
| Epochs | 1 |
| Batch Size | 8 |
| Training Time | ~30 minutes |

> **Note:** This experiment used the dataset's test split for fine-tuning rather than the training split. Therefore, the original test split should not be used to report an unbiased evaluation score for this model.

## Dataset

The model was fine-tuned on:

**Gretel AI Synthetic Text-to-SQL**

https://huggingface.co/datasets/gretelai/synthetic_text_to_sql

The dataset contains natural-language questions paired with SQL queries and database context.

Approximately **5,850 examples from the test split** were used for this fine-tuning experiment.

## Fine-Tuning Approach

The training process used QLoRA:

1. The Llama 3.2 3B base model was loaded in 4-bit precision.
2. The original base model weights remained frozen.
3. LoRA adapters with rank 16 were trained on the Text-to-SQL dataset.
4. Training was performed for one epoch.
5. The trained LoRA adapter was merged with the base model.
6. The merged model was also converted to Q4_K_M GGUF for local inference.

This approach significantly reduces the memory required for fine-tuning compared with full-parameter training.
## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "farehaheha/llama3.2-3B-text-to-sql"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
)

prompt = """### Database Schema:
{your_database_schema}

### Request:
{your_natural_language_request}

### SQL Query:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=False,
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)