Github link - https://github.com/rejzzzz/NyayMitra

LLaMA 3.2 - Fine-tuned on Indian Law Dataset (QLoRA)

This model is a fine-tuned version of Meta-LLaMA 3.2 3B using QLoRA on Indian Law datasets.
It is designed to assist in legal question answering, case law summarization, and other NLP tasks in the Indian legal domain.

Base Model

Meta-LLaMA 3.2 3B

Fine-tuning Approach

QLoRA using PEFT (Parameter Efficient Fine-Tuning)
Trained on AWS SageMaker

Dataset Used

Indian Law Dataset
viber1/indian-law-dataset
LLM Fine Tuning Dataset of Indian Legal Texts
Kaggle Dataset

Training Details

Framework: PyTorch, Transformers
Hardware: AWS SageMaker (ml.g5.xlarge instance - 24gb RAM and 100gb EBS Volume)
Epochs: 3
Learning Rate: -
LoRA Rank: -

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-username/llama-3.2-indianlaw-lora"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "What is Article 21 of the Indian Constitution?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

rej06
/

NyayMitra