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Cataloged from unsloth/GLM-4.7-Flash-REAP-23B-A3B-GGUF
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𓌳 REAP𓌳 the Experts: Why Pruning Prevails for One-Shot MoE Compression
Introducing GLM-4.7-Flash-REAP-23B-A3B, a memory-efficient compressed variant of GLM-4.7-Flash that maintains near-identical performance while being 25% lighter.
This model was created using REAP (Router-weighted Expert Activation Pruning), a novel expert pruning method that selectively removes redundant experts while preserving the router's independent control over remaining experts. Key features include:
GLM-4.7-Flash-REAP-23B-A3B has the following specifications:
🟩 This checkpoint maintains almost identical performance while being 25% lighter.
For more details on the evaluation setup, refer to the REAP arXiv preprint.
You can deploy the model directly using the latest vLLM (that supports GLM4.7-Flash), no source modifications or custom patches required.
If you encounter insufficient memory when running this model, you might need to set a lower value for --max-num-seqs flag (e.g. set to 64).
This checkpoint was created by applying the REAP (Router-weighted Expert Activation Pruning) method uniformly across all Mixture-of-Experts (MoE) blocks of GLM-4.7, with a 25% pruning rate.
REAP selects experts to prune based on a novel saliency criterion that considers both:
This dual consideration ensures that experts contributing minimally to the layer's output are pruned, while preserving those that play critical roles in the model's computations.
The model was calibrated using a diverse mixture of domain-specific datasets including:
📚 For more details, refer to the following resources:
This model is derived from
zai-org/GLM-4.7-Flash
and distributed under the MIT license.
If you use this checkpoint, please cite the REAP paper:
vllm serve cerebras/GLM-4.7-Flash-REAP-23B-A3B \ --tensor-parallel-size 4 \ --reasoning-parser glm45 \ --tool-call-parser glm47 \ --enable-auto-tool-choice@article{lasby-reap, title={REAP the Experts: Why Pruning Prevails for One-Shot MoE compression}, author={Lasby, Mike and Lazarevich, Ivan and Sinnadurai, Nish and Lie, Sean and Ioannou, Yani and Thangarasa, Vithursan}, journal={arXiv preprint arXiv:2510.13999}, year={2025}}