Introducing Gemma-4-19B-A4B-text-REAP, a memory-efficient compressed variant of Gemma-4-26B-A4B-it 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:
Near-Lossless Performance: Maintains almost identical accuracy on code generation, agentic coding, and function calling tasks compared to the full 26B model
25% Memory Reduction: Compressed from 26B to 19B parameters, significantly lowering deployment costs and memory requirements
Preserved Capabilities: Retains all core functionalities including code generation, agentic workflows, repository-scale understanding, and function calling
Drop-in Compatibility: Works with vanilla vLLM - no source modifications or custom patches required
Optimized for Real-World Use: Particularly effective for resource-constrained environments, local deployments, and academic research
You can either specify a new local-dir (gemma-4-19B-A4B-text-REAP) or download them all in place (./)
🧩 Model Creation
This GGUF was created by applying the REAP (Router-weighted Expert Activation Pruning) method uniformly across all Mixture-of-Experts (MoE) blocks of gemma-4-26B-A4B-it, with a 25% pruning rate.
How REAP Works
REAP selects experts to prune based on a novel saliency criterion that considers both:
Router gate values: How frequently and strongly the router activates each expert
Expert activation norms: The magnitude of each expert's output contributions
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.
Key Advantages
One-Shot Compression: No fine-tuning required after pruning - the model is immediately ready for deployment
Preserved Router Control: Unlike expert merging methods, REAP maintains the router's independent, input-dependent control over remaining experts, avoiding "functional subspace collapse"
Generative Task Superiority: REAP significantly outperforms expert merging approaches on generative benchmarks (code generation, creative writing, mathematical reasoning) while maintaining competitive performance on discriminative tasks
📚 For more details, refer to the following resources:
1@article{lasby-reap,
2 title={REAP the Experts: Why Pruning Prevails for One-Shot MoE compression},
3 author={Lasby, Mike and Lazarevich, Ivan and Sinnadurai, Nish and Lie, Sean and Ioannou, Yani and Thangarasa, Vithursan},
4 journal={arXiv preprint arXiv:2510.13999},
5 year={2025}
6}
