| | --- |
| | 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 |
| | --- |
| | # 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** | |
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| | <img width="1090" height="590" alt="image" src="https://github.com/user-attachments/assets/662ea336-8946-4542-b2f2-eb78712d5a2d" /> |
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| | --- |
| |
|
| | ## 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**. |
