metadata

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.