Text Generation
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text-generation-inference

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 βˆ’0.95
PIQA 68.4 63.55 βˆ’4.85
CommonsenseQA 33.9 33.42 βˆ’0.48
TriviaQA 4.1 0.0 βˆ’4.10
Winogrande 51.3 51.78 +0.48
OpenBookQA 34.6 30.80 βˆ’3.80
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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