SmolMath-135M
SmolMath is a full finetuned version of SmolLM2-135M parameter, trained to obtain the highest math accuracy, with least drop in other text benchmarks.
Important: All training codes are present in the Github Important: Please refer to the Blog for methodology and Training details.
Usage
model_path = "Ashed00/SmolMath-135M" # Path where your fine-tuned model is saved
from transformers import pipeline
pipe = pipeline("text-generation", model=model_path)
question = "What is 2+2?"
prompt = "Question: " + question + "\nAnswer:"
output = pipe(
prompt,
max_length=100,
do_sample=False, # disable sampling for greedy decoding
)[0]["generated_text"]
Evaluation and Performance
Comparision with Base Model
Metrics | SmolLM2-135M-8k | SmolMath-135M | Ξ (Change) |
---|---|---|---|
HellaSwag | 42.1 | 41.15 | β0.95 |
PIQA | 68.4 | 63.55 | β4.85 |
CommonsenseQA | 33.9 | 33.42 | β0.48 |
TriviaQA | 4.1 | 0.0 | β4.10 |
Winogrande | 51.3 | 51.78 | +0.48 |
OpenBookQA | 34.6 | 30.80 | β3.80 |
GSM8K (0-shot)* | 0.0 | 6.9 | +6.90 |
*This was evaluated using the lighteval script, which is favoured by the SmolLM2 creators in their evaluation and varies from the SmolMath prompt structure.
Math Benchmarks
Model | AddSub* (%) | MAWPS** (%) | GSM8K* (%) |
---|---|---|---|
apple/OpenELM-270M-Instruct | 2.14 | 2.83 | 2.05 |
HuggingFaceTB/SmolLM2-135M-Instruct | 1.52 | 4.04 | 0.45 |
SmolMath-no GRPO (ours) | 9.64 | 7.47 | 6.22 |
SmolMath (ours) | 12.05 | 8.31 | 7.51 |
*Evaluated only on the test set, not included in the training **Evaluated on complete dataset, not included in the training
Citation
Incase you want to use this model in your work, you can site us.
@misc{SmolMath,
title = {Building SmolMath: A Math Reasoning SLM Under 150M Parameters},
url = {https://hackmd.io/@ashu-00/SmolMath},
author = {ashu-00},
month = {July},
year = {2025}
}
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Base model
HuggingFaceTB/SmolLM2-135M