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Qwen3.5-88B
REAP-pruned Qwen/Qwen3.5-122B-A10B.
At a glance
| Base model | Qwen/Qwen3.5-122B-A10B |
| Format | BF16 |
| Total params | 88B |
| Active / token | 10B |
| Experts / layer | — |
| Layers | — |
| Hidden size | — |
| Context | — |
| On-disk size | 175 GB |
Which variant should I pick?
| Variant | Format | Link |
|---|---|---|
Qwen3.5-264B | BF16 | link |
Qwen3.5-264B-FP8 | FP8 | link |
Qwen3.5-264B-W4A16 | W4A16 | link |
Qwen3.5-28B | BF16 | link |
Qwen3.5-35B-EXL3-4bpw | EXL3-4bpw | link |
Qwen3.5-76B | BF16 | link |
Qwen3.5-76B-GGUF | GGUF | link |
Qwen3.5-88B (this) | BF16 | link |
Qwen3.5-99B | BF16 | link |
Qwen3.5-99B-GGUF | GGUF | link |
30% expert-pruned variant of Qwen3.5-122B-A10B using REAP (Routing-Enhanced Activation Pruning).
Model Details
| Property | Value |
|---|---|
| Base Model | Qwen/Qwen3.5-122B-A10B |
| Architecture | Qwen3.5 MoE (GDN + Full Attention) |
| Original Experts | 256 per layer |
| Pruned Experts | 180 per layer (30% removed) |
| Active Parameters | ~10B per token |
| Pruning Method | REAP with targeted refusal preservation |
| Preserve Threshold | 80% (super-expert protection) |
| Calibration | reap-calibration-data-v1 — 23k benchmark-free samples |
| Maintainer | 0xSero |
| Organization | Sybil Solutions |
| Project | REAP PR17 |
Usage
vllm serve 0xSero/Qwen3.5-88B \
--tensor-parallel-size 4 \
--enable-expert-parallel \
--max-model-len 8192 \
--trust-remote-code \
--language-model-only \
--dtype bfloat16
Important: Use --language-model-only flag — this is a text-only checkpoint pruned from the multimodal base model.
What is REAP?
REAP (Routing-Enhanced Activation Pruning) removes the least-activated experts from MoE models while preserving critical capabilities. It uses router activation patterns from a calibration dataset to identify dispensable experts, with special protection for safety-critical behaviors.
License
Same license as the base model (Qwen).
License & citation
License inherited from the base model.
@misc{lasby2025reap,
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},
year = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
}
Sponsors
Made possible by NVIDIA · TNG Technology · Lambda · Prime Intellect · Hot Aisle.