---
base_model: unsloth/Qwen3-0.6B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen3-0.6B
- lora
- sft
- transformers
- trl
- unsloth 
license: mit
language:
- en
datasets:
- musaoc/Quran-reasoning-SFT
---

# 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.

```python
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