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README.md

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 base_model:
 - Qwen/Qwen3.5-397B-A17B
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+Model: Qwen3.5-397B-A17B (Qwen's latest Mixture-of-Experts multimodal model)
+What was done:
+
+Applied REAP (Router-weighted Expert Activation Pruning) from Cerebras Research to prune 55% of MoE experts
+Original model: 512 experts per layer, 10 active per token
+Pruned model: 230 experts per layer, 10 active per token
+Observation phase: 128 calibration samples from evol-codealpaca-v1 dataset, cosine similarity scoring, seed 42
+Pruning method: frequency-based (experts ranked by activation frequency across calibration data, bottom 55% removed)
+
+Key details:
+
+Original model size: 752GB (BF16)
+Pruned safetensors: ~377GB (BF16)
+GGUF Q3_K_M: ~72GB (estimated)
+Architecture: 60 transformer layers, fused MoE experts (gate_up_proj + down_proj), linear attention + full attention pattern, Mamba SSM components
+Experts reduced from [512, 2048, 4096] → [230, 2048, 4096] per layer
+Router weights sliced accordingly
+
+Tools used:
+
+REAP: https://github.com/cerebras/reap
+llama.cpp for GGUF conversion and quantization
+
+Based on research:
+
+Cerebras REAP paper — shows 50%+ expert pruning retains 95%+ baseline quality on code generation and reasoning tasks