Model Card for Model ID

Model Details

This model is a fine-tuned version of Qwen/Qwen3-0.6B on the musaoc/Quran-reasoning-SFT dataset. It is designed to perform reasoning and question-answering tasks related to the Quran, providing structured reasoning steps along with the final answer.

Model Description

Language(s) (NLP): English
License: MIT
Fine-tuning method: Supervised fine-tuning (SFT)
Finetuned from model: Qwen3-0.6B
Dataset: musaoc/Quran-reasoning-SFT

Uses

The model is intended for:

Educational purposes: Assisting with structured reasoning about Quranic content.
Research: Exploring reasoning capabilities of small LLMs fine-tuned on religious text.
QA Systems: Providing answers with reasoning traces.

Not intended for:

Authoritative religious rulings (fatwas)
Sensitive or controversial theological debates
High-stakes decision making

Out-of-Scope Use

Scope: The model is limited to the reasoning dataset it was trained on. It may not generalize to broader Quranic studies.

Bias, Risks, and Limitations

Bias: Outputs reflect dataset biases and may not represent all scholarly interpretations.
Hallucination risk: Like all LLMs, it may generate incorrect or fabricated reasoning.
Religious sensitivity: Responses may not align with every sect, school, or interpretation. Use with caution in sensitive contexts.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-0.6B",)
base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/Qwen3-0.6B",
    device_map={"": 0}
)

model = PeftModel.from_pretrained(base_model,"Rustamshry/Quran-R1")

question = "How does the Quran address the issue of parental authority and children’s rights?"

messages = [
    {"role" : "user",   "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
    enable_thinking = True,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 512,
    temperature = 0.6,
    top_p = 0.95,
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

Training Data

Dataset: musaoc/Quran-reasoning-SFT

The Quranic Reasoning Question Answering (QRQA) Dataset is a synthetic dataset designed for experimenting purposes and for training and evaluating models capable of answering complex, knowledge-intensive questions about the Quran with a strong emphasis on reasoning. This dataset is particularly well-suited for Supervised Fine-Tuning (SFT) of Large Language Models (LLMs) to enhance their understanding of Islamic scripture and their ability to provide thoughtful, reasoned responses.

Framework versions

PEFT 0.17.0

Rustamshry
/

Quran-R1