MLX-compatible REAP for pruning MoE models on Apple Silicon
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Sep 6, 2026 - Python
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expert-pruning
Here are 7 public repositories matching this topic...
MoE expert compression toolkit — prune, merge, and forge specialist models for local inference.
machine-learning pytorch moe pruning quantization model-compression mixture-of-experts llm llama-cpp vllm local-llm llm-inference gguf expert-pruning
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Sep 4, 2026 - Python
Less is MoE (EMNLP 2026 Main, Oral); Fisher-MoE: intra-expert structured pruning and compression for Mixture-of-Experts (MoE) LLMs. Prunes FFN intermediate dimensions by Fisher importance instead of dropping whole experts. Official code.
moe pruning model-compression efficient-inference fisher-information mixture-of-experts neuron-pruning structured-pruning large-language-models llm vllm qwen llm-compression emnlp2026 olmoe expert-pruning
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Sep 8, 2026 - Python
KL-divergence + blind task eval of GLM-4.7-Flash and its REAP-pruned variant vs gpt-oss-20b on llama.cpp
moe glm quantization kl-divergence runpod llama-cpp local-llm gguf llm-evaluation gpt-oss expert-pruning
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Jun 12, 2026 - Python
How I fit 35B-parameter MoE models into 16 GB of consumer VRAM: REAP expert pruning + NVFP4 quantization on an RTX 5070 Ti (SM120), served on vLLM, with measured evals.
moe quantization reap mixture-of-experts blackwell vllm llm-inference consumer-gpu nvfp4 expert-pruning
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Sep 9, 2026
REAP pruning + NVFP4 quantization of Ornith-1.5-35B-A3B for 16GB VRAM
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Sep 3, 2026 - Python
Serving a large MoE coder model on consumer Blackwell GPUs via REAP expert pruning + NVFP4 quantization.
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Sep 3, 2026 - Python
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