Bielik v3 Small: Technical Report
Authors: name: Krzysztof Ociepa, name: Łukasz Flis, name: Remigiusz Kinas, name: Krzysztof Wróbel, name: Adrian Gwoździej

We introduce Bielik v3, a series of parameter-efficient generative text
models (1.5B and 4.5B) optimized for Polish language processing. These models
demonstrate that smaller, well-optimized architectures can achieve performance
comparable to much larger counterparts while requiring substantially fewer
computational resources. Our approach incorporates several key innovations: a
custom Polish tokenizer (APT4) that significantly improves token efficiency,
Weighted Instruction Cross-Entropy Loss to balance learning across instruction
types, and Adaptive Learning Rate that dynamically adjusts based on training
progress. Trained on a meticulously curated corpus of 292 billion tokens
spanning 303 million documents, these models excel across multiple benchmarks,
including the Open PL LLM Leaderboard, Complex Polish Text Understanding
Benchmark, Polish EQ-Bench, and Polish Medical Leaderboard. The 4.5B parameter
model achieves results competitive with models 2-3 times its size, while the
1.5B model delivers strong performance despite its extremely compact profile.
These advances establish new benchmarks for parameter-efficient language
modeling in less-represented languages, making high-quality Polish language AI
more accessible for resource-constrained applications.