khazarai
/

Quran-R1

@@ -9,202 +9,101 @@ tags:
 - transformers
 - trl
 - unsloth
 ---
-# Model Card for Model ID
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 ## How to Get Started with the Model
 Use the code below to get started with the model.
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 ### Framework versions
 - PEFT 0.17.0

 - transformers
 - trl
 - unsloth
+license: mit
+datasets:
+- musaoc/Quran-reasoning-SFT
+language:
+- en
 ---
+# Model Card for Quran-R1
 ## 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,"khazarai/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