This is an implementation of Alpaca-LoRa that uses a custom variant of the Alpaca dataset focused on Wealth/Finance. The training is done using Kaggle's free GPUs (2x Tesla T4s).

To install the required packages, run:

pip install -r requirements.txt

To generate data, run:

python -m generate_data generate_instructions_from_websites

This will generate data from the sources listed in sources.txt.

You can add your own sources to sources.txt.

Make sure to run python -m generate_data again after adding new sources to sources.txt.

More details on the dataset can be found here

The training script wealth-alpaca_lora.py is optimized for training on Kaggle using 2x Tesla T4s and can be run through:

!torchrun --nproc_per_node=2 wealth-alpaca_lora.py

Training notebook on Kaggle can be found here: https://www.kaggle.com/code/gbhacker23/wealth-alpaca-lora

LoRa Weights: https://huggingface.co/gbharti/finance-alpaca-lora

Inference: https://colab.research.google.com/drive/1lIlJpsMq4JP1GszUw_oZhyKr2irsMcq_?usp=sharing

Interface for inference: https://colab.research.google.com/drive/11V_w7y5hEbVgsx9we8RMyapY9bEeuY5X?usp=sharing

Comparison between Alpaca-LoRa and Wealth Alpaca-LoRa: https://docs.google.com/document/d/1ldso1tmFLkg0flePdDKj_iTc8gcmdRtoe9n1dYN1W10/edit?usp=sharing

ChatGPT responses: https://docs.google.com/document/d/1PevbI89jgiPYaSSCnVcLIF9ObiDbAf_LsmsTx-SIvBg/edit?usp=sharing