Awesome

Finetuning LLaMa + Text-to-SQL

This walkthrough shows you how to fine-tune LLaMa 2 7B on a Text-to-SQL dataset, and then use it for inference against any database of structured data using LlamaIndex.

Check out our full blog here: https://medium.com/llamaindex-blog/easily-finetune-llama-2-for-your-text-to-sql-applications-ecd53640e10d

This code is taken and adapted from the Modal doppel-bot repo: https://github.com/modal-labs/doppel-bot.

Stack

• LlamaIndex
• Modal
• Hugging Face datasets
• OpenLLaMa
• Peft

Setup

To get started, clone or fork this repo:

git clone https://github.com/run-llama/modal_finetune_sql.git

Steps for Running

Please load the notebook tutorial.ipynb for full instructions.

cd modal_finetune_sql
jupyter notebook tutorial.ipynb

In the meantime you can run each step individually as below:

Loading data: modal run src.load_data_sql

Finetuning: modal run --detach src.finetune_sql

Inference: modal run src.inference_sql_llamaindex::main --query "Which city has the highest population?" --sqlite-file-path "nbs/cities.db"

(Optional) Downloading model weights: modal run src.download_weights --output-dir out_model