README.md

---
base_model: unsloth/gemma-2-9b-bnb-4bit
library_name: peft
license: apache-2.0
datasets:
- microsoft/orca-math-word-problems-200k
- MathQA
metrics:
- accuracy
pipeline_tag: question-answering
tags:
- math
- gemma
- 'LoRA '
---

# Model Card for Model ID

This model is based on the Gemma-2-9b architecture and has been fine-tuned using two math problem datasets to improve its accuracy in solving mathematical tasks.

## Datasets

1. **[Orca-Math](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k)**:  
   A dataset containing approximately 200K grade school math word problems, with answers generated using Azure GPT-4 Turbo.
   Designed to help models solve elementary-level math problems. 
2. **[MathQA](https://math-qa.github.io/)**:  
   An annotated dataset of math word problems derived from the AQuA-RAT dataset using a novel representation language.  
   The dataset includes questions, multiple-choice options, rationales, and correct answers.
   
## Training Details

The training process included:
- Optimizer: AdamW (8-bit)
- Learning Rate: 2e-4
- Epochs: 1 epoch for Orca-Math, 3 epochs for MathQA
- Batch Size: 16
- Compute Resources: The model was fine-tuned using a single GPU (A100 80GB) for 14 hours.
- Fine-tuning Method: LoRA was used for efficient training and parameter reduction.
- Framework: Fine-tuning was conducted using Unsloth, enabling faster training and better memory efficiency.

## Evaluation
The model was evaluated using the **MathQA test dataset** with **accuracy** as the primary metric. The following table compares its performance to other models:

| Model                | Accuracy (%)  |
|----------------------|---------------|
| Gemma-2-9b (base)    | 24.02       |
| Mistral-7B-Instruct   | 22.61       |
| Llama-3.1-8b-Instruct | 27.37      |
| Llama-3.2-3b-Instruct | 23.48      |
| Qwen2.5-7B-Instruct  | 38.69         |
| **mathGemma-2-9b**  | **42.479**    |


## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dasool/math_gemma-2-9b")
model = AutoModelForCausalLM.from_pretrained("Dasool/math_gemma-2-9b")

# Example usage
inputs = tokenizer("Solve: 12 + 7", return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_length=30)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Limitations

The evaluation is based solely on accuracy for a 5-option multiple-choice task. This provides a high-level performance metric but does not fully capture the model's reasoning ability or performance on more complex, open-ended math problems. Deeper analysis is required to explore the model's problem-solving skills.


##  Model Card Contact

If you have any questions or feedback, feel free to contact:
- Email: [email protected]