---
base_model: QCRI/Fanar-1-9B-Instruct
datasets: AI-MO/NuminaMath-TIR
library_name: peft
model_name: Fanar-0.5B-GRPO-test
tags:
- generated_from_trainer
- trl
- grpo
- math
- reasoning
- R1
licence: license
license: apache-2.0
language:
- ar
- en
pipeline_tag: text-generation
---

# 🧠 Fanar-Math-R1-GRPO

**Fanar-Math-R1-GRPO** is a reasoning-optimized language model built on [`QCRI/Fanar-1-9B-Instruct`](https://huggingface.co/QCRI/Fanar-1-9B-Instruct). This version is fine-tuned using **Group Relative Policy Optimization (GRPO)** from the DeepSeekMath framework on the [`AI-MO/NuminaMath-TIR`](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. It is designed for step-by-step mathematical problem-solving with structured reasoning in both English and Arabic.


![image/png](https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/NEcy5S2aYn2ly2filngUp.png)


---

## 🚀 Model Highlights

- 🔁 Fine-tuned with **GRPO**, a sample-efficient reinforcement learning method
- 🧮 Specializes in **multi-step mathematical reasoning**
- 💬 Outputs responses in a structured conversational format using `<think>` and `<answer>` tags
- 🧠 Trained using **TRL** (`transformers`, `peft`, and `math_verify`)
- 🏷️ Useful for both instruction-following and math-heavy dialogue generation

---

## 📦 Model Details

| Component        | Description                                                                 |
|------------------|-----------------------------------------------------------------------------|
| **Base Model**   | [`QCRI/Fanar-1-9B-Instruct`](https://huggingface.co/QCRI/Fanar-1-9B-Instruct) |
| **Fine-Tuning**  | GRPO via Hugging Face [TRL](https://github.com/huggingface/trl)              |
| **Dataset**      | [`AI-MO/NuminaMath-TIR`](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) |
| **Format**       | `<think> ... </think> <answer> ... </answer>` tagged reasoning structure     |
| **LoRA**         | Enabled (modules: `q_proj`, `v_proj`, rank=8)                               |
| **Epochs**       | 1 (lightweight test configuration)                                           |
| **Tokenizer**    | Same as base model                                                           |

---

## 🧪 Inference Example

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import time

model_id = "Omartificial-Intelligence-Space/Fanar-Math-R1-GRPO"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

def generate_with_reasoning(prompt_text):
    inputs = tokenizer(prompt_text, return_tensors="pt").to(model.device)
    start = time.time()
    with torch.no_grad():
        output = model.generate(**inputs, max_length=1024)
    end = time.time()

    generated = tokenizer.decode(output[0], skip_special_tokens=True)
    duration = end - start
    num_input_tokens = inputs["input_ids"].shape[1]
    num_generated_tokens = output.shape[1] - num_input_tokens

    return generated, duration, num_generated_tokens

# Example Arabic math problem
prompt_text = '''في مدينة يبلغ عدد سكانها 1 مليون نسمة، إذا كان 60% من السكان بالغين، و40% من البالغين يعملون، فكم عدد العاملين في المدينة؟'''

result, time_taken, tokens = generate_with_reasoning(prompt)
print(result)
```

---

## 🛠️ Training Setup

### Configuration Summary

- **learning_rate**: 1e-5
- **epochs**: 1
- **max_completion_length**: 64
- **num_generations**: 4
- **gradient_accumulation_steps**: 16
- **logging_steps**: 10

### Reward Functions

- **accuracy_reward**: validates correctness of the answer using `math_verify`
- **format_reward**: checks for proper usage of `<think>` and `<answer>` tags

### Libraries & Versions

```
transformers==4.47.1
trl==0.14.0
peft==0.14.0
datasets==2.21.0
math_verify==0.3.3
torch==2.4.1
```
---

## 📚 Output Format

The model is trained to follow a reasoning-first format:

```
<think> أولاً، نحسب 60% من مليون نسمة، وهو 600,000. ثم نحسب 40% من هذا العدد، وهو 240,000. </think>
<answer> 240,000 </answer>
```

---

## 🔬 Citations

### GRPO – DeepSeekMath

```bibtex
@article{zhihong2024deepseekmath,
  title={DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models},
  author={Shao, Zhihong and Wang, Peiyi and Zhu, Qihao and Xu, Runxin and Song, Junxiao and Zhang, Mingchuan and Li, Y.K. and Wu, Y. and Guo, Daya},
  journal={arXiv preprint arXiv:2402.03300},
  year={2024}
}
```

### TRL Library

```bibtex
@misc{vonwerra2022trl,
  title={TRL: Transformer Reinforcement Learning},
  author={von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  year={2022},
  howpublished={\url{https://github.com/huggingface/trl}}
}
```
```
@misc{fanarllm2025,
      title={Fanar: An Arabic-Centric Multimodal Generative AI Platform}, 
      author={Fanar Team and Ummar Abbas and Mohammad Shahmeer Ahmad and Firoj Alam and Enes Altinisik and Ehsannedin Asgari and Yazan Boshmaf and Sabri Boughorbel and Sanjay Chawla and Shammur Chowdhury and Fahim Dalvi and Kareem Darwish and Nadir Durrani and Mohamed Elfeky and Ahmed Elmagarmid and Mohamed Eltabakh and Masoomali Fatehkia and Anastasios Fragkopoulos and Maram Hasanain and Majd Hawasly and Mus'ab Husaini and Soon-Gyo Jung and Ji Kim Lucas and Walid Magdy and Safa Messaoud and Abubakr Mohamed and Tasnim Mohiuddin and Basel Mousi and Hamdy Mubarak and Ahmad Musleh and Zan Naeem and Mourad Ouzzani and Dorde Popovic and Amin Sadeghi and Husrev Taha Sencar and Mohammed Shinoy and Omar Sinan and Yifan Zhang and Ahmed Ali and Yassine El Kheir and Xiaosong Ma and Chaoyi Ruan}},
      year={2025},
      url={https://arxiv.org/abs/2501.13944}, 
}
```
---

## 🔗 Resources

- [DeepSeekMath Paper](https://arxiv.org/abs/2402.03300)
- [TRL Documentation](https://huggingface.co/docs/trl)
- [Open-R1 Project](https://github.com/huggingface/open-r1)

---

Happy reasoning! 🔍✨