| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - Qwen/Qwen2.5-Math-1.5B |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - math |
| - qwen |
| - gsm8k |
| - lora |
| - mathematics |
| datasets: |
| - openai/gsm8k |
| --- |
| # OpenMath |
| Fine-tuning a Small Language Model (SLM) for Step-by-Step Math Reasoning |
|
|
| ## Overview |
| **OpenMath** is an open-source project focused on fine-tuning a **small language model (SLM)** to solve **math word problems** with clear, step-by-step reasoning. |
| The project uses **LoRA/QLoRA fine-tuning** on popular math reasoning datasets and provides a benchmarking pipeline to compare performance against other open-source SLMs/LLMs. |
|
|
| This project is designed to be reproducible on **free Colab (T4) GPU**. |
|
|
| --- |
|
|
| ## What’s Included |
| - QLoRA fine-tuning code (4-bit) |
| - GSM8K subset training (example: 1k samples) |
| - GSM8K evaluation script (accuracy) |
| - Saved LoRA adapter weights |
|
|
| --- |
|
|
| ## Base Model |
| - **Qwen2.5-Math-1.5B** |
|
|
| --- |
|
|
| ## Dataset |
| - **GSM8K** (Grade School Math 8K) |
| - Training used: **1000 samples** |
| - Evaluation: GSM8K test split |
|
|
| --- |
|
|
| ## Results |
| ### Training Setup (Current) |
| - Samples: 1000 |
| - Epochs: 6 |
| - Max length: 1024 |
| - LoRA rank: 16 |
| - Loss masking: trained mainly on the **solution portion** to improve reasoning |
|
|
| ### Accuracy |
| - **GSM8K Accuracy (100-sample test subset): 41%** |
|
|
| > Note: The 41% score was measured on a **100-question subset** of the GSM8K test set for faster evaluation on Colab. |
|
|
| --- |
|
|
| ## GSM8K Leaderboard (Baseline) |
|
|
| | Model | Params | GSM8K Accuracy (%) | |
| |------|--------|---------------------| |
| | LLaMA 2 | 13B | 28.7 | |
| | Gemma 2 (PT) | 2B | 23.9 | |
| | Mistral (Base) | 7B | 36.5 | |
| | ERNIE 4.5 | 21B | 25.2 | |
| | Baichuan (Base) | 13B | 26.6 | |
| | Gemma | 7B | 46.4 | |
| | Zephyr-7b-gemma-v0.1 | 7B | 45.56 | |
| | LLaMA 3.2 Instruct (CoT) | 1B | 39.04 | |
| | Gemma 3 IT | 1B | 42.15 | |
| | Qwen 3 (Instruct mode) | 1.7B | 33.66 | |
| | **OpenMath (Qwen2.5-Math-1.5B + LoRA)** | **1.5B** | **41.0** | |
|
|
|
|
|
|
|
|
| <img width="1090" height="590" alt="image" src="https://github.com/user-attachments/assets/662ea336-8946-4542-b2f2-eb78712d5a2d" /> |
|
|
|
|
|
|
|
|
|
|
| --- |
|
|
| ## Repository Files |
| ### LoRA Adapter Folder |
| This project provides the fine-tuned adapter weights: |
|
|
| - `adapter_model.safetensors` → LoRA weights |
| - `adapter_config.json` → LoRA configuration |
|
|
| > Note: This is **not a full model**. |
| > You must load the **base model** and then attach the adapter. |
|
|
| --- |
|
|
| ## Disclaimer |
| OpenMath is an educational/research project. |
|
|
| The fine-tuned model may produce incorrect, incomplete, or misleading answers. |
|
|
| Always verify solutions independently before using them for exams, assignments, or real-world decisions. |
|
|
| This project does **not** guarantee correctness and should not be used as a substitute for professional advice. |
|
|
| --- |
|
|
| ## Contributing |
| Contributions are welcome! 🎉 |
|
|
| If you’d like to contribute: |
| 1. Fork the repository |
| 2. Create a new branch (`feature/your-feature-name`) |
| 3. Commit your changes |
| 4. Open a Pull Request |
|
|
| ### Contribution Ideas |
| - Run full GSM8K test evaluation (1319 samples) and report results |
| - Train on larger GSM8K subsets (3k–5k samples) |
| - Add SVAMP / ASDiv datasets for better generalization |
| - Improve decoding to reduce repetition |
| - Add a Streamlit demo for interactive testing |
| - Benchmark against more open-source SLMs/LLMs |
| - Improve evaluation scripts and metrics |
|
|
| --- |
|
|
| ## Note |
| OpenMath is a **fun and practical side project** built to explore **efficient fine-tuning (QLoRA)** and **math reasoning evaluation** on limited compute. |
|
|
| The goal is to learn, experiment, and share reproducible results — while keeping the code clean and open for community improvements. |
|
|
| --- |
|
|
| ## License |
| This project is licensed under the **Apache License 2.0**. |
