This research design proposal presents a cross-lingual knowledge distillation framework for compressing large multilingual language models, specifically targeting European languages. Building upon the Bielik-Minitron-7B foundation, the method employs structured pruning combined with language-balanced distillation to achieve 33% parameter reduction while maintaining cross-lingual capabilities. The proposed three-stage pipeline includes importance-aware pruning, temperature-annealed distillation, and alignment via SFT and RLHF. Evaluation plans encompass XNLI, XCOPA, and Open PL benchmarks across Polish, Czech, Hungarian, and other languages.
Key findings
Structured pruning achieves 33.4% parameter reduction (11.04B → 7.35B) with 90.1% performance recovery versus teacher model
Language-balanced sampling strategy prevents high-resource language dominance during cross-lingual distillation
Progressive temperature annealing (2.0 → 1.0) captures cross-lingual dark knowledge and improves low-resource transfer
Three-stage pipeline targets 50% inference speedup on consumer GPUs while maintaining multilingual capabilities
Hybrid depth-and-width pruning with activation-based importance estimation enables efficient architecture search
Limitations & open questions
Proposed method awaits empirical validation on full multilingual benchmark suite
Heavy computational requirements (16× NVIDIA H200 GPUs) may limit reproducibility and accessibility
Risk of catastrophic forgetting in low-resource European languages during aggressive compression