machinez/Qwen3.6-35B-REAP-Pruned-ratio-0.5

Qwen3.6-35B-A3B REAP Pruned Ratio 0.5

This model was converted to GGUF format from RangerX/Qwen3.6-35B-REAP-Pruned-ratio-0.5 using llama.cpp's llama-imatrix & llama-quantize. It only has Q3KM. For Q4KM see https://huggingface.co/lennyhans/Qwen3.6-35B-REAP-Pruned-ratio-0.5-Q4KM-GGUF.

This repository contains a REAP-pruned version of Qwen/Qwen3.6-35B-A3B. The checkpoint was produced with routed-expert pruning using REAP (Router-weighted Expert Activation Pruning), which scores routed experts with router weights and expert activation norms.

Pruning Settings

Calibration Data

The calibration set used the REAP paper/code mixture with 1024 total samples:

• —theblackcat102/evol-codealpaca-v1: 171 samples
• —Salesforce/xlam-function-calling-60k: 171 samples
• —open-r1/Mixture-of-Thoughts[code]: 171 samples
• —open-r1/Mixture-of-Thoughts[math]: 171 samples
• —open-r1/Mixture-of-Thoughts[science]: 170 samples
• —SWE-bench/SWE-smith-trajectories(tool): 170 samples

Integration Notes

This checkpoint was generated with packed Qwen3.5/Qwen3.6 REAP support. The packed routed expert tensors and router rows were sliced while preserving the shared expert and the vision-language configuration. The saved model uses the Transformers qwen3_5_moe architecture and includes tokenizer and processor files.

Citation

@inproceedings{
    lasby2026reap,
    title={{REAP} the Experts: Why Pruning Prevails for One-Shot MoE compression},
    author={Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
    booktitle={The Fourteenth International Conference on Learning Representations},
    year={2026},
    url={https://openreview.net/forum?id=ukGxWd2aDG}
}