A 4B compiler model generates LoRA adapters from natural-language specs, enabling a frozen 0.6B interpreter to match Qwen3-32B performance on fuzzy text tasks at 50× less memory.
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Continual pre-training on a German medical corpus lets 7B models close much of the performance gap with 24B general models on medical benchmarks, though merging introduces some language mixing and verbosity.
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Can Continual Pre-training Bridge the Performance Gap between General-purpose and Specialized Language Models in the Medical Domain?
Continual pre-training on a German medical corpus lets 7B models close much of the performance gap with 24B general models on medical benchmarks, though merging introduces some language mixing and verbosity.