NYUAD-ComNets/NYUAD_Llama4_Inheritance_Solver

Model Description

import os
import torch
import pandas as pd
from datasets import Dataset
from trl import SFTTrainer
from huggingface_hub import login
import re
from peft import LoraConfig, get_peft_model
import numpy as np
from transformers import (
    AutoTokenizer,
    Llama4ForConditionalGeneration,
    BitsAndBytesConfig,
    TrainingArguments,
    DataCollatorForLanguageModeling,
    AutoModelForCausalLM
)

#should install transformers 4.51.3

hf_token = "xxxxxxxxxxxxxxxxxxxxxxxxxxxe"
login(hf_token)

model_id = "NYUAD-ComNets/NYUAD_Llama4_Inheritance_Solver"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = Llama4ForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)

# Template for inference prompt
inference_prompt_template = """Answer the following question using a single word only from this list A, B, C, D, E, F. Do not add details.
### Context:
{}

### Response:
{}"""  

def generate_answer(context):
    prompt = inference_prompt_template.format(context, "")
    inputs = tokenizer(prompt + tokenizer.eos_token, return_tensors="pt").to("cuda")

    with torch.no_grad():
        outputs = model.generate(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            max_new_tokens=10,
            eos_token_id=tokenizer.eos_token_id,
            use_cache=True,
            temperature =0.1,
            top_p=1
        )

    response = tokenizer.batch_decode(outputs, skip_special_tokens=True)
    print(response)

    response =   response[0].split("### Response:")[1][-1]


df=pd.read_csv('/path_to/islamic_inheritance_problem.csv.csv')
for k,o1,o2,o3,o4,o5,o6 in zip(df.question.values
                                ,df.option1.values,df.option2.values
                                ,df.option3.values,df.option4.values
                                ,df.option5.values,df.option6.values):
    
    
        example = k+' '+o1+' '+o2+' '+o3+' '+o4+' '+o5+' '+o6
        
        predicted_label = generate_answer(example)
        print("Predicted:", predicted_label)

Training hyperparameters

• Low-Rank Adaptation (LoRA)
• training = 7 epochs
• learning rate = 0.0002
• batch sizes = 1 per device
• gradient accumulation steps = 1.
• The optimizer "paged_adamw_32bit"
• 10 warmup steps
• 4-bit quantization with the NormalFloat 4 format

Model Card Contact

[email protected]

BibTeX entry and citation info

@article{aldahoul2025benchmarking,
  title={Benchmarking the Legal Reasoning of LLMs in Arabic Islamic Inheritance Cases},
  author={AlDahoul, Nouar and Zaki, Yasir},
  journal={arXiv preprint arXiv:2508.15796},
  year={2025}
}