| | --- |
| | base_model: QCRI/Fanar-1-9B-Instruct |
| | datasets: AI-MO/NuminaMath-TIR |
| | library_name: transformers |
| | 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 |
| | --- |
| | |
| | # 🧠 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. |
| |
|
| | --- |
| |
|
| | ## 🚀 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 = """A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer either in Arabic or English based on user's language. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer> 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 |
| | ``` |
| | |
| | --- |
| | |
| | ## 📊 Training Metrics (Snapshot) |
| | |
| | | Step | Reward (avg) | Accuracy Reward | Format Reward | Loss | KL Divergence | |
| | |------|--------------|-----------------|---------------|-------|---------------| |
| | | 10 | 0.029 | 0.029 | 0.0 | 0.0 | 0.00024 | |
| | | 100 | 0.039 | 0.039 | 0.0 | 0.0001| 0.00188 | |
| | | 200 | 0.033 | 0.033 | 0.0 | 0.0001| 0.00183 | |
| | | 300 | 0.045 | 0.045 | 0.0 | 0.0001| 0.00127 | |
| | |
| | *Note: Training was run with a small config for notebook-friendly experimentation.* |
| | |
| | --- |
| | |
| | ## 📚 Output Format |
| | |
| | The model is trained to follow a reasoning-first format: |
| | |
| | ``` |
| | <think> First, we calculate 60% of 1 million, which is 600,000. Then, 40% of that is 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}} |
| | } |
| | ``` |
| | |
| | --- |
| | |
| | ## 🔗 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) |
| | |
| | --- |
| | |
| | ## 🧑🔬 Authors |
| | |
| | Developed and trained by **Omar Paniego** with adaptation of the DeepSeek-R1 training recipe using Hugging Face's open tools and datasets. |
| |
|
| | --- |
| |
|
| | ## 📢 License |
| |
|
| | Refer to the license file in the repository. |
| |
|
| | --- |
| |
|
| | ## ❤️ Acknowledgements |
| |
|
| | Thanks to: |
| | - **Hugging Face Science Team** for `trl` and `math_verify` |
| | - **AI-MO** for the NuminaMath-TIR dataset |
| | - **DeepSeek Team** for releasing their methodology and insights |
| |
|
| | Happy reasoning! 🔍✨ |
| | ## Citations |
| |
|
| | Cite GRPO as: |
| |
|
| | ```bibtex |
| | @article{zhihong2024deepseekmath, |
| | title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, |
| | author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, |
| | year = 2024, |
| | eprint = {arXiv:2402.03300}, |
| | } |
| | |
| | ``` |
| |
|
| | Cite TRL as: |
| | |
| | ```bibtex |
| | @misc{vonwerra2022trl, |
| | title = {{TRL: Transformer Reinforcement Learning}}, |
| | author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, |
| | year = 2020, |
| | journal = {GitHub repository}, |
| | publisher = {GitHub}, |
| | howpublished = {\url{https://github.com/huggingface/trl}} |
| | } |
| | ``` |
