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This article introduces an optimization scheme for large language models that combines post-training pruning and Low-Rank Adaptation (LoRA). It achieves model compression through structured weight pruning while maintaining model accuracy, providing a feasible path for the efficient deployment of LLMs.
Section 01
This article introduces an optimization scheme for large language models that combines post-training structured pruning and Low-Rank Adaptation (LoRA). It achieves model compression through structured weight pruning while maintaining accuracy, and LoRA provides efficient adaptation capabilities, offering a feasible path for the efficient deployment of LLMs.
Section 02
As the parameter scale of LLMs grows to hundreds of billions, inference costs and deployment difficulties increase exponentially, making it hard to run in resource-constrained environments. Model compression technology has become a focus: traditional pruning needs to be done during training, which has high computational overhead; post-training pruning allows compression after training is completed without retraining from scratch.
Section 03
This project adopts a strategy combining post-training structured pruning (removing entire neurons/channels/attention heads, hardware-friendly) and LoRA. Structured pruning restores performance through iterative evaluation of structural importance and fine-tuning; LoRA introduces low-rank matrices to update original weights, reducing fine-tuning resources. The two work synergistically: pruning compresses volume, and LoRA provides efficient adaptation capabilities.
Section 04
The compression ratio and accuracy are balanced through progressive pruning + fine-tuning, sensitivity analysis, and knowledge distillation. Experiments show that when 30%-50% of parameters are pruned, performance drops by <2%, and inference speed increases by 1.5-2 times.
Section 05
Applicable to edge device deployment (real-time inference + personalized adaptation), cloud inference optimization (cost reduction + rapid customization), and multi-tenant environments (more instance deployments).
Section 06
Limitations: Structured pruning has lower compression efficiency than unstructured pruning and requires support from specific inference engines; Future directions: fine-grained structured pruning, combination with quantization technology, development of dedicated inference kernels, and deep integration of pruning and LoRA.
Section 07
This scheme paves the way for the widespread deployment of LLMs, and developers need to master compression optimization techniques. With improved hardware support and algorithmic progress, LLMs will become more accessible.
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