FlameF0X / MathGPT2

MathGPT-2 (distilgpt2 Fine-Tuned for Arithmetic)

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

Benchmark

Link here .

Model Description

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

Key Features:

• Solves basic arithmetic (addition, subtraction, multiplication, division)
• Can handle simple problems like 12 + 5 =
• Fine-tuned version of distilgpt2 on a math-specific dataset
• Trained for 10 epochs (further improvements can be made by training for more epochs)

Model Details

• Model architecture : DistilGPT-2
• Training duration : 10 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).
• It can generate answers for simple problems like 12 * 6 = ? .

Example:

Input :

13 + 47 =

Output :

60

Benchmark Results

We evaluated the model using a set of 10000 randomly generated math expressions to assess its performance. Here are the results:

• Accuracy : 76.3%
• Average Inference Time : 0.1448 seconds per question

Training Data

The training dataset was generated using Python, consisting of random arithmetic expressions (addition, subtraction, multiplication, division) between numbers from 1 to 100. The expressions were formatted as:

2 + 3 = 5
100 - 25 = 75
45 * 5 = 225
100 / 25 = 4

No duplicate expressions were used, ensuring the model learns unique patterns.

Fine-Tuning

This model was fine-tuned from the distilgpt2 base model for 100 epochs.

Limitations

• Basic Arithmetic Only : The model can only handle basic arithmetic problems like addition, subtraction, multiplication, and division. 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.