---
base_model: Qwen/Qwen3.6-35B-A3B
library_name: transformers
license: apache-2.0
tags:
- qwen3.6
- moe
- reap
- pruning
- bitsandbytes
---

# Qwen3.6-35B-A3B REAP Pruned Ratio 0.3 with Pre-REAP BNB4 Scoring

This model is derived from `Qwen/Qwen3.6-35B-A3B` using REAP routed-expert pruning with a pruning ratio of 0.3. Saliency scores were collected from a pre-REAP `bitsandbytes` 4-bit scoring model, then the original BF16 checkpoint was reloaded, pruned, and saved.

## Pruning Setup

- Base model: `Qwen/Qwen3.6-35B-A3B`
- Method: REAP routed-expert pruning
- Pre-REAP scoring model: `bitsandbytes` 4-bit NF4, BF16 compute, double quantization enabled
- Final checkpoint dtype: saved from the original full-precision/BF16 model after pruning; this is not a quantized checkpoint
- Pruning ratio: 0.3
- Routed experts before pruning: 256 per MoE layer
- Routed experts pruned: 76 per MoE layer
- Routed experts retained: 180 per MoE layer
- Shared experts: preserved
- Calibration samples: 1024
- Sequence length: 2048
- Seed: 42
- Router renormalization: true

## Notes

This checkpoint uses the packed Qwen3.5/Qwen3.6 REAP integration. The bnb4 quantized model was used only for saliency score collection; pruning and saving were applied to the original model weights.