REVIEW 7 cited by
MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Mixture-of-Experts (MoE) architectures face challenges such as high memory consumption and redundancy in experts. Pruning MoE can reduce network weights while maintaining model performance. Motivated by the recent observation of emergent large magnitude features in Large Language Models (LLM) and MoE routing policy, we propose MoE-Pruner, a method that prunes weights with the smallest magnitudes multiplied by the corresponding input activations and router weights, on each output neuron. Our pruning method is one-shot, requiring no retraining or weight updates. We evaluate our method on Mixtral-8x7B and Mixtral-8x22B across multiple language benchmarks. Experimental results show that our pruning method significantly outperforms state-of-the-art LLM pruning methods. Furthermore, our pruned MoE models can benefit from a pretrained teacher model through expert-wise knowledge distillation, improving performance post-pruning. Experimental results demonstrate that the Mixtral-8x7B model with 50% sparsity maintains 99% of the performance of the original model after the expert-wise knowledge distillation.
Forward citations
Cited by 7 Pith papers
-
It Takes a MAESTRO To Prune Bad Experts
Pruning MoE LLMs according to the stationary distribution of a Markov chain over (layer, expert) routing transitions retains more task performance than local importance heuristics, with up to ~3.5% relative gains over...
-
Train Large, Deploy Compact: Structured Compression for Compact Low-Rank Adaptation
PrunedLoRA tunes large-rank LoRA adapters and progressively structurally prunes A and B to a target low rank, empirically closing much of the LoRA-to-full-fine-tuning gap.
-
Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs
DERN prunes SMoE LLMs by decomposing removed experts into neuron segments, reassigning the best-matching ones to kept experts, and clustering them into compact replacements, beating prior pruning baselines without retraining.
-
Utility-Driven Speculative Decoding for Mixture-of-Experts
Cascade is a utility-driven speculation manager that makes speculative decoding practical for MoE LLMs by disabling it when expert-activation cost exceeds token gain and hill-climbing to the best speculation length.
-
Automatic Pruning Discovery for Large Language Models
AutoPrune uses LLM-driven search to generate a pruning formula and SDSA to allocate sparsity by layer skewness, reporting small perplexity gains over Wanda and SparseGPT on several LLaMA models.
-
LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference
LExI sets a different number of active experts per layer, found by weight-only sensitivity profiling and evolutionary search, improving MoE inference throughput with little accuracy loss.
-
Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging
Sub-MoE compresses MoE LLMs by K-means clustering of experts plus frequency-weighted merging of right singular vectors after a shared SVD, and claims 96 and 86 percent retained accuracy at 25 and 50 percent expert red...
Discussion (0). Sign in to comment.