---
tags:
- mathematics
- addition
- subtraction
license: apache-2.0
library_name: transformers
accuracy_add: 0.999999
accuracy_sub: 0.99999
train_loss: 4.7e-08.
---
# QuantaMaths: `mix_d8_l3_h4_t60K_s173289`

This repository contains a transformer model that can predict both addition and subtraction questions.

### Model-specific metadata
- **Operation type**: mixed
- **Num digits**: 8
- **Layers**: 3
- **Attention Heads**: 4
- **Training steps**: 60,000
- **Random seed**: 173289

**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.