Large language models (LLMs) demonstrate strong performance across natural language processing tasks, yet undergo significant performance degradation when modified for deployment through quantization, pruning, or decoding strategy adjustments. We define this phenomenon as model hemorrhage - performance decline caused by parameter alterations and architectural changes. Through systematic analysis of various LLM frameworks, we identify key vulnerability patterns: layer expansion frequently disrupts attention mechanisms, compression techniques induce information loss cascades, and decoding adjustments amplify prediction divergences. Our investigation reveals transformer architectures exhibit inherent robustness thresholds that determine hemorrhage severity across modification types. We propose three mitigation strategies: gradient-aware pruning preserves critical weight pathways, dynamic quantization scaling maintains activation integrity, and decoding calibration aligns generation trajectories with original model distributions. This work establishes foundational metrics for evaluating model stability during adaptation, providing practical guidelines for maintaining performance while enabling efficient LLM deployment. Our findings advance understanding of neural network resilience under architectural transformations, particularly for large-scale language models.

翻译：大语言模型（LLMs）在自然语言处理任务中展现出强大性能，但在通过量化、剪枝或解码策略调整进行部署修改时，会出现显著的性能下降。我们将此现象定义为模型出血——由参数变更和架构改动引起的性能衰退。通过对多种LLM框架的系统性分析，我们识别出关键脆弱性模式：层扩展频繁破坏注意力机制，压缩技术引发信息损失级联，而解码调整则放大预测分歧。我们的研究表明，Transformer架构表现出固有的鲁棒性阈值，该阈值决定了不同修改类型下的出血严重程度。我们提出三种缓解策略：梯度感知剪枝以保留关键权重路径，动态量化缩放以维持激活完整性，以及解码校准以使生成轨迹与原始模型分布对齐。这项工作建立了评估模型在适应过程中稳定性的基础指标，为在实现高效LLM部署的同时保持性能提供了实用指南。我们的发现推进了对神经网络在架构变换下（尤其是大规模语言模型）韧性的理解。