Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Math-1.5B
+pipeline_tag: question-answering
+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** |
+
+
+
+
+<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
+- Tokenizer + template files for correct formatting
+
+> 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**.  
+See the [LICENSE](LICENSE) file for details.