| --- |
| tags: |
| - mathematics |
| - addition |
| - subtraction |
| license: apache-2.0 |
| library_name: transformers |
| accuracy_add: 0.999999 |
| accuracy_sub: 0.999999 |
| train_loss: 5e-09. |
| --- |
| # QuantaMaths: `mix_d6_l3_h4_t40K_s372001` |
| |
| This repository contains a transformer model that can predict both addition and subtraction questions. |
| |
| ### Model-specific metadata |
| - **Operation type**: mixed |
| - **Num digits**: 6 |
| - **Layers**: 3 |
| - **Attention Heads**: 4 |
| - **Training steps**: 40,000 |
| - **Random seed**: 372001 |
| |
| **Contents**: |
| - `model.pth`: The trained transformer model. |
| - `training_loss.json`: Data gathered during model training (used to plot "loss over training batches"). |
| - `behaviors.json`: Facts gathered about the model by direct inspection (attention pattern data, PCA data, digit impact data, etc.). |
| - `features.json`: Facts gathered about hypothesized algorithm features via experimentation, e.g. node P12L0H1 implements the feature A3.ST. |
|
|
| **Provenance**: |
| - `model.pth` and `training_loss.json` were created by [QuantaMathsTrain.ipynb](https://github.com/PhilipQuirke/quanta_maths/blob/main/notebooks/QuantaMathsTrain.ipynb). |
| - `behaviors.json` and `features.json` were created by [QuantaMathsAnalyse.ipynb](https://github.com/PhilipQuirke/quanta_maths/blob/main/notebooks/QuantaMathsAnalyse.ipynb). |
| - The JSON files are used by [QuantaMathsAlgorithm.ipynb](https://github.com/PhilipQuirke/quanta_maths/blob/main/notebooks/QuantaMathsAlgorithm.ipynb). |
|
|
| **Folder name details**: |
| - "add", "sub", or "mix": The types of questions the model can predict. |
| - "d5" to "d20": How many digits the model handles (e.g. a d5 sub model can predict the answer in 123450-345670=-0123230). |
| - "l1", "l2", or "l3": The number of layers in the model. |
| - "h3" or "h4": The number of attention heads in the model. |
| - "t15K" to "t85K", etc.: The number of batches the model was trained on. |
| - "s372001", etc.: The random seed used in model training. |
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