---
datasets:
- wikisql
pipeline_tag: text-generation
tags:
- llama
---

# AI2sql

AI2sql is a state-of-the-art LLM for converting natural language questions to SQL queries.

# Model Card: Fine-tuning Llama 2 for AI2SQL Query Generation

This model card outlines the fine-tuning of the Llama 2 model to generate SQL queries for AI2SQL tasks.

## Model Details
- **Original Model:** NousResearch/Llama-2-7b-chat-hf
- **Model Type:** Large Language Model
- **Fine-tuning Task:** AI2SQL (SQL Query Generation)
- **Fine-tuned Model Name:** llama-2-7b-miniguanaco

## Implementation
- **Environment Requirement:** GPU-supported platform with minimum 20GB RAM.
- **Dependencies:** accelerate==0.21.0, peft==0.4.0, bitsandbytes==0.40.2, transformers==4.31.0, trl==0.4.7
- **GPU Specification:** T4 or equivalent (as of 24 Aug 2023)

## Training Details
- **Dataset:** WikiSQL
- **Method:** Supervised Fine-Tuning (SFT)
- **Epochs:** 1
- **Batch Size:** 4 per GPU
- **Optimization:** AdamW with cosine learning rate schedule
- **Learning Rate:** 2e-4
- **Special Features:**
  - LoRA for efficient parameter adjustment.
  - 4-bit precision model loading with BitsAndBytes.
  - Gradient checkpointing and clipping.

## Performance Metrics
- **Accuracy:** 85% (on a held-out test set from WikiSQL)
- **Query Generation Time:** Average of 0.5 seconds per query
- **Resource Efficiency:** Demonstrates 30% reduced memory usage compared to the base model

## Usage and Applications
TBD


Note: The performance metrics provided here are hypothetical and for illustrative purposes only. Actual performance would depend on various factors, including the specifics of the dataset and training regimen.