---
license: mit
base_model:
- distilbert/distilgpt2
tags:
- text-generation-inference
library_name: transformers
datasets:
- FlameF0X/Math
---

# MathGPT-2 Kaly (distilgpt2 Fine-Tuned for Arithmetic and basic Geometry)

This model is a **fine-tuned version of DistilGPT-2** on a custom dataset consisting exclusively of arithmetic and geometric problems and their answers. The goal of this model is to act as a **calculator** that can solve basic arithmetic problems.

## Benchmark
Link [Not avaliable]().

## Model Description

The model was trained using a dataset of decent arithmetic expressions, including addition, subtraction, multiplication, division, powers root and geometric. The training data was generated using Python and ensured to have **no duplicate expressions**.

### Key Features:
- **Solves basic arithmetic** (addition, subtraction, multiplication, division, powers root, geometric)
- Can **handle simple problems** like `12 + 5 =`
- Fine-tuned version of `distilgpt2` on a math-specific dataset
- Trained for **1 epochs** (further improvements can be made by training for more epochs)

## Model Details

- **Model architecture**: DistilGPT-2
- **Training duration**: 1 epochs (could be improved further)
- **Dataset**: Generated math expressions like `12 + 5 = 17`
- **Tokenization**: Standard GPT-2 tokenizer
- **Fine-tuned on**: Simple arithmetic operations

## Intended Use

This model is designed to:
- **Answer basic arithmetic problems** (addition, subtraction, multiplication, division, powers root, geometric).
- It can generate answers for simple problems like `12 * 6 = ?`.

### Example:

**Input**:
```
13 + 47 =
```

**Output**:
```
60
```
## Fine-Tuning

This model was fine-tuned from the `distilgpt2` base model for 1 epochs.

---

## Limitations

- **Basic Arithmetic Only**: The model can only handle basic arithmetic problems like addition, subtraction, multiplication, division, powers root, simple geometric. It does not handle more complex operations like exponentiation, logarithms, or advanced algebra.
- **Limited Training Duration**: While trained for 10 epochs, more epochs or data diversity may improve the model's performance further.
- **No real-time validation**: The model's performance varies, and there are still inaccuracies in answers for some problems.