This work was accomplished when Liyi Chen was an intern at Alibaba Cloud Computing.

One-shot pruning techniques offer a way to alleviate these burdens by removing redundant weights without the need for retraining. Y et, the massive scale of LLMs often forces current pruning approaches to rely on heuristics instead of optimization-based techniques, potentially resulting in suboptimal compression.

The most extensive model described to date is the base model size encompasses 77.6M parameters

LLMs' proficiency as judges for coding tasks using a curated, human-annotated dataset, exploring their evaluation capabilities and potential biases, including whether they favor their own generated solutions. Our findings underscore the potential of these benchmarks to advance LLM development and application in coding assistance.