🧮 NanoCalc-1M

NanoCalc-1M is a ultra-compact, character-level Seq2Seq Transformer based on the T5 architecture, specifically trained to perform arithmetic operations with high precision.

• Architecture: T5-based Encoder-Decoder
• Parameters: 0.99M
• Precision: Mixed Precision (BF16/FP16)
• Vocab: Character-level (0-9, +, -, *, /, =)
• Training Data: 2,000,000 synthetic samples (3-digit arithmetic)
• Max Input Length: 20 tokens
• Performance: ~97% Accuracy on 4-operation math (Validation Set)

Performance Chart

Epoch	Training Loss	Val Accuracy	Status
1	1.1420	54.89%	🔴 Learnt Format
2	0.3931	78.79%	🟡 Learnt Digits
5	0.1638	91.91%	🟢 Learning subtleties
9	0.1051	97.15%	🔵 High Precision
10	0.1004	97.73%	🚀 Near Perfect

How to use

To use this model, download model.pt and use.py and run it on any type of device with Python3.

Examples

Model loaded (Accuracy: 97.73% from epoch 10)

--- Mini Math Model interactive --- Enter an arithmetic task (e.g. 15*15) or type 'exit' to quit this.

Task > 0*567 Model: 0 | Correct: 0 ✅

Task > 999+999 Model: 1998 | Correct: 1998 ✅

Task > 1/1 Model: 1 | Correct: 1 ✅

Task > 1684*8787 Model: 6398 | Correct: 14797308 ❌

Task > 124*598 Model: 2452 | Correct: 74152 ❌

Task > 12/68 Model: 4 | Correct: 0 ❌

Task > 123*123 Model: 499 | Correct: 15129 ❌

Task > 47*5 Model: 235 | Correct: 235 ✅

Task > 456+125 Model: 581 | Correct: 581 ✅

Task > 957-234 Model: 723 | Correct: 723 ✅

Task > 120-7650 Model: -550 | Correct: -7530 ❌

Task > 450-750 Model: -300 | Correct: -300 ✅

Task > 453-97 Model: 356 | Correct: 356 ✅

Task > 129-462 Model: -333 | Correct: -333 ✅

Task > 8*8 Model: 64 | Correct: 64 ✅

Task > 54*54 Model: 2916 | Correct: 2916 ✅

Task > 102*78 Model: 748 | Correct: 7956 ❌

Task > 74*9 Model: 666 | Correct: 666 ✅

Task > 103-34 Model: 69 | Correct: 69 ✅

Overall accuracy

The overall accuracy after 10 epochs of training is ~97% for tasks with max. 3 digits each like 74*9 or 103-34.

Limitations

The can't do:

• Tasks with more than 3 digits like 3984-125
• Multiplication tasks with numbers above 99 like 293*21
• Complex tasks

Training

We trained for 10 epochs (~20 minutes of training on Kaggle 2x T4) with 2 million randomly generated training samples.

Final thoughts

We may be releasing an improved version of this that can solve really complex tasks and much more...stay tuned!