---
base_model: Qwen/Qwen3.6-35B-A3B
pipeline_tag: text-generation
tags:
- qwen3.6
- mixture-of-experts
- expert-pruning
- reap
- text-generation
---

# Qwen3.6-35B-REAP Pruned ratio 0.2

This is a REAP-pruned checkpoint derived from `Qwen/Qwen3.6-35B-A3B`.
The pruning ratio is `0.20`.

## Pruning

The model was pruned with REAP routed-expert pruning. Expert saliency was computed
from router weights and expert activation norms on a 1024-sample calibration set
with sequence length 2048. Router weights were renormalized after pruning.

Calibration data used the paper-style composite mixture:

- `theblackcat102/evol-codealpaca-v1`
- `Salesforce/xlam-function-calling-60k`
- `open-r1/Mixture-of-Thoughts[code]`
- `open-r1/Mixture-of-Thoughts[math]`
- `open-r1/Mixture-of-Thoughts[science]`
- `SWE-bench/SWE-smith-trajectories(tool)`

The checkpoint keeps the shared expert path unchanged. The routed MoE layers keep
`205` experts per layer and `num_experts_per_tok=8`.

## REAP integration notes

Qwen3.5/Qwen3.6 use a packed MoE layout, so the REAP pipeline was extended with
architecture-specific adapters for locating MoE modules, collecting packed-expert
activation metrics, slicing routed expert tensors and router rows, and saving
reloadable Hugging Face checkpoints while preserving tokenizer and processor files.

## Details

- Base model: `Qwen/Qwen3.6-35B-A3B`
- Pruning method: `reap`
- Pruning ratio: `0.20`
- Calibration samples: 1024
- Calibration sequence length: 2048
- Seed: 42
- Router renormalization: true
- Local checkpoint size before upload: 54G

Use the model with `trust_remote_code=True`.