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https://github.com/nisaaragharia/indian-lawyergpt

Fine-Tuning Falcon-7B, LLAMA 2 with QLoRA to create an advanced AI model with a profound understanding of the Indian legal context.
https://github.com/nisaaragharia/indian-lawyergpt

falcon fine-tuning gpt huggingface-transformers large-language-models llama llama2 llms peft qlora

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Fine-Tuning Falcon-7B, LLAMA 2 with QLoRA to create an advanced AI model with a profound understanding of the Indian legal context.

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README

        

# Indian Law AI: Fine-Tuning Falcon-7B & LLAMA 2 Language Models

Welcome to our exciting project where we are adapting two cutting-edge language models, Falcon-7B & LLAMA 2, to become proficient in Indian law.

## Overview

Our adventure began with a modest 150 Q&As on Indian law. Now, we're charging ahead with an impressive dataset of 3300 instructions! This AI legal project combines:

- **Falcon-7B & LLAMA 2**: State-of-the-art language models, prepped and ready for legal training.
- **PEFT & QLoRA**: The dream duo for memory-efficient and high-performance model fine-tuning.
- **[Our Dataset](https://huggingface.co/datasets/nisaar/Articles_Constitution_3300_Instruction_Set)**: Comprehensive Indian law knowledge, spanning constitutional law, civil rights, and more!

## Research Paper
![image](https://github.com/NisaarAgharia/Indian-LawyerGPT/assets/22457544/6bc73d7a-c19b-41d6-ae20-3a5787644001)

## Dataset Creation
Dataset Creation (3)

## Dive into our Dataset

Our dataset is designed with four key features: `instruction`, `input`, `output`, and `prompt`. Crafted to shape our models into AI law experts!
Dataset on Hugging Face :
https://huggingface.co/datasets/nisaar/Constitution_Of_India_Instruction_Set
https://huggingface.co/datasets/nisaar/Articles_Constitution_3300_Instruction_Set
https://huggingface.co/datasets/nisaar/LLAMA2_Legal_Dataset_4.4k_Instructions

## Fine Tuning Process
Fine Tuning

## Track the Progress

Get a front-row seat to the training progress with TensorBoard. Kickstart it, navigate to the provided localhost link, and witness the models learn: