annotations_creators:
- machine-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: SLM-Bench
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
- text-generation
task_ids:
- multiple-choice-qa
- language-modeling
SLM-Bench: A Benchmark for Sub-10M Language Models
SLM-Bench is a comprehensive, fully programmatic benchmark designed specifically for evaluating sub-10M parameter language models. It covers six core capability areas with 500 questions each (3,000 total), providing granular insights into model strengths and weaknesses.
Dataset Summary
| Category | Questions | Description |
|---|---|---|
| arithmetic | 500 | Math problems with plausible distractors (off-by-one, sign errors) |
| pattern | 500 | Sequence completion (arithmetic, geometric, fibonacci, cubic progressions) |
| grammar | 500 | Grammatical correctness MCQs (tense, articles, prepositions, pronouns) |
| vocabulary | 500 | WordNet-based questions (antonyms, synonyms, definitions, categories) |
| logic | 500 | Syllogism and logical reasoning (valid/invalid inference detection) |
| word_analogy | 500 | Word relationship analogies (antonym, hypernym, part-to-whole, function) |
Data Splits
Each category is split into:
- train: 200 examples (for few-shot prompting)
- validation: 150 examples (primary evaluation)
- test: 150 examples (hold-out evaluation)
Total: 3,000 questions across all categories.
Data Format
Each example follows a consistent multiple-choice schema:
{
"id": "arithmetic_0001",
"question": "What is 47 + 23?",
"choices": ["70", "69", "71", "68"],
"answer": 0,
"category": "arithmetic",
"difficulty": "easy"
}
Usage with lm-evaluation-harness
SLM-Bench is registered as a task suite in lm-evaluation-harness:
# Full benchmark
lm_eval --model hf \
--model_args pretrained=liodon-ai/slm-10m,trust_remote_code=True \
--tasks slm_bench \
--device cuda:0 --batch_size 64
# Single category
lm_eval --model hf \
--model_args pretrained=liodon-ai/slm-10m,trust_remote_code=True \
--tasks slm_bench_arithmetic \
--device cuda:0
# All subtasks individually
lm_eval --model hf \
--model_args pretrained=liodon-ai/slm-10m,trust_remote_code=True \
--tasks slm_bench_tasks \
--device cuda:0
Usage with HuggingFace Datasets
from datasets import load_dataset
# Load a specific category
ds = load_dataset("liodon-ai/slm-bench", "arithmetic")
print(ds["test"][0])
# {'id': 'arithmetic_0164', 'question': 'What is (43 + 5) * 5 - 1?',
# 'choices': ['239', '240', '932', '229'], 'answer': 0, ...}
# Load all categories
for category in ["arithmetic", "pattern", "grammar", "vocabulary", "logic", "word_analogy"]:
ds = load_dataset("liodon-ai/slm-bench", category)
print(f"{category}: {len(ds['test'])} test examples")
Generation
The dataset is fully programmatically generated — no manual annotation. The generation scripts are open-source at github.com/Liodon-AI/slm-bench.
To regenerate:
pip install nltk numpy datasets huggingface_hub
python assemble.py --n_per_category 500 --seed 42
Why SLM-Bench?
Existing benchmarks (ARC, HellaSwag, MMLU) are designed for models with billions of parameters. SLM-Bench fills the gap by providing:
- Appropriate difficulty — calibrated for sub-10M models
- Diverse capabilities — arithmetic, language, logic, patterns
- Plausible distractors — wrong answers that test real understanding, not just pattern matching
- Fully reproducible — deterministic generation with fixed seed
- Zero manual effort — entirely algorithmic, no human annotation bias
Citation
@dataset{slm_bench_2024,
author = {Liodon AI},
title = {SLM-Bench: A Benchmark for Sub-10M Language Models},
year = {2024},
url = {https://huggingface.co/datasets/liodon-ai/slm-bench},
publisher = {Hugging Face}
}
License
Apache-2.0
Contact
- Organization: Liodon AI
- Website: https://liodon.ai
- GitHub: github.com/Liodon-AI