Post
1943
We have released the first version of CompactLMIndex UniversalComputingResearch/clmi, a curated benchmark for smaller language models.
The index evaluates models across six benchmarks covering commonsense, science and world knowledge, and grammatical competence. Scores are normalized for random chance, so results are easier to compare across tasks with different baselines.
The composite gives 50% weight to commonsense, 25% to science and world knowledge, and 25% to grammatical competence. BLiMP is macro-averaged across its 12 linguistic categories. Every result includes 95% bootstrap intervals, and rank ranges reflect paired comparisons on matched evaluation items.
The goal is to compare compact models under one fixed, zero-shot evaluation protocol and make differences in architecture, training, and inference visible without treating parameter count as a substitute for quality.
If you have results for a model that fits the benchmark scope, please open a discussion on the Space.
The index evaluates models across six benchmarks covering commonsense, science and world knowledge, and grammatical competence. Scores are normalized for random chance, so results are easier to compare across tasks with different baselines.
The composite gives 50% weight to commonsense, 25% to science and world knowledge, and 25% to grammatical competence. BLiMP is macro-averaged across its 12 linguistic categories. Every result includes 95% bootstrap intervals, and rank ranges reflect paired comparisons on matched evaluation items.
The goal is to compare compact models under one fixed, zero-shot evaluation protocol and make differences in architecture, training, and inference visible without treating parameter count as a substitute for quality.
If you have results for a model that fits the benchmark scope, please open a discussion on the Space.