---
language:
- en
license: apache-2.0
tags:
- text-to-sql
- llama-3
- lora
- spider
- sql-generation
- fine-tuned
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- spider
metrics:
- exact_match
- execution_accuracy
---

# 🤖 Nano-Analyst: Fine-Tuned SQL Agent

**A private, on-device SQL generation model fine-tuned on the Spider dataset.**

## Model Description

This is a **LoRA fine-tuned** version of **Llama-3-8B-Instruct** specialized for Text-to-SQL generation. The model was trained on 6,300 examples from the Spider dataset and achieves 100% valid SQL generation.

### Key Features

- ✅ **Privacy-Preserving**: Runs locally, no API calls required
- ✅ **Efficient**: QLoRA fine-tuning (only 1.03% parameters trained)
- ✅ **Production-Ready**: Includes self-correction and RAG retrieval
- ✅ **Well-Documented**: Complete training and evaluation pipeline

## Model Details

- **Base Model**: meta-llama/Meta-Llama-3-8B-Instruct
- **Fine-Tuning Method**: QLoRA (4-bit quantization)
- **Framework**: Unsloth (2x faster training)
- **Training Data**: Spider dataset (6,300 training examples)
- **LoRA Rank**: 32
- **LoRA Alpha**: 64

## Training Results

- **Final Training Loss**: 0.19
- **Validation Loss**: 0.31
- **Training Steps**: 1,182
- **Training Time**: ~3 hours on T4 GPU

## Evaluation Results

Evaluated on 100 examples from Spider validation set:

| Metric | Value |
|--------|-------|
| **Valid SQL Generation** | 100.0% |
| **Exact String Match** | 2.0% |
| **Successful Queries** | 100/100 |

## Usage

### Loading the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Meta-Llama-3-8B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA adapters
model = PeftModel.from_pretrained(
    base_model,
    "tanvicas/nano-analyst-sql"
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
```

### Generating SQL

```python
question = "How many users are from California?"
schema = "CREATE TABLE users (id INT, name TEXT, state TEXT);"

# Generate SQL using the model
sql = generate_sql(question, schema)
print(sql)  # SELECT COUNT(*) FROM users WHERE state = 'California';
```

## License

Apache 2.0 License. Base model subject to Meta's Llama 3 Community License.

## Contact

- **GitHub**: github.com/tanvicas/nano-analyst

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