---

license: apache-2.0

base_model: 0xSero/Qwen3.5-88B

library_name: gguf

pipeline_tag: text-generation

tags:

• gguf
• abliterated
• qwen3.5
• moe
• reap
• not-for-all-audiences

---

Qwen3.5-122B-A10B-REAP-30 - Abliterated GGUF

Abliterated, importance-matrix (imatrix) quantized GGUFs of

0xSero/Qwen3.5-88B -

0xSero's ~30% MoE expert-prune (REAP) of Qwen/Qwen3.5-122B-A10B,

taking the model from 122B down to ~88B parameters while keeping the A10B active-expert budget and

the qwen35moe architecture. Robinson Labs then abliterated the pruned model and quantized it here.

Unlike the full 122B sibling, this REAP variant has no Multi-Token Prediction (MTP / NextN): the

upstream config declared a phantom nextn layer carrying no weights, so our convert produced a clean

48-layer model (block_count=48). These are standard single-token-prediction GGUFs.

A full-precision bf16 safetensors base for re-abliteration, LoRA merge, fine-tune, or rolling your

own quants is at RobinsonLabs/Qwen3.5-122B-A10B-REAP-30-abliterated.

Disclosure

This model is abliterated - the hard-refusal reflex on adult / creative content has been

reduced via single-direction weight orthogonalization. Harm guardrails are retained by design:

self-harm prompts still redirect to help (e.g. 988), and it is not intended to assist genuine

wrongdoing. This is a v1, partial abliteration; capability is preserved. Tagged

not-for-all-audiences. Use responsibly - you are responsible for your use. License inherited from

the base model: Apache-2.0.

Files

| File | Quant | ~Size | Notes |

|---|---|---|---|

| Qwen3.5-122B-A10B-REAP-30-abliterated-Q6_K.gguf | Q6_K | ~67 GB | near-lossless |

| Qwen3.5-122B-A10B-REAP-30-abliterated-Q5_K_M.gguf | Q5_K_M | ~58 GB | |

| Qwen3.5-122B-A10B-REAP-30-abliterated-Q4_K_M.gguf | Q4_K_M | ~50 GB | |

| Qwen3.5-122B-A10B-REAP-30-abliterated-Q4_K_S.gguf | Q4_K_S | ~47 GB | |

| Qwen3.5-122B-A10B-REAP-30-abliterated-IQ4_XS.gguf | IQ4_XS | ~44 GB | quality/size sweet spot |

| Qwen3.5-122B-A10B-REAP-30-abliterated-Q3_K_M.gguf | Q3_K_M | ~39 GB | |

| Qwen3.5-122B-A10B-REAP-30-abliterated-IQ3_M.gguf | IQ3_M | ~36 GB | |

| Qwen3.5-122B-A10B-REAP-30-abliterated-IQ3_XS.gguf | IQ3_XS | ~34 GB | |

| Qwen3.5-122B-A10B-REAP-30-abliterated-IQ2_M.gguf | IQ2_M | ~27 GB | |

| Qwen3.5-122B-A10B-REAP-30-abliterated-IQ2_XS.gguf | IQ2_XS | ~24 GB | smallest |

All quants are imatrix-weighted (generic calibration). This is the REAP-pruned 48-layer model - no

MTP block.

!Quant ladder - bits-per-weight vs file size

Method

• Expert prune (REAP): ~30% of the MoE experts removed by 0xSero's REAP method, 122B -> ~88B

params, qwen35moe arch, A10B active budget retained.

• Abliteration: single mid-layer refusal direction removed via weight orthogonalization on the

bf16 pruned base; routers preserved. No MTP/NextN block exists in this variant.

• Quant: importance-matrix (imatrix) weighted convert + quantize with

llama.cpp. The imatrix here uses a **generic calibration

set** (bartowski calibration_datav3) - a broad, domain-agnostic fit.

bf16 base

The full-precision bf16 safetensors base for this ladder is

RobinsonLabs/Qwen3.5-122B-A10B-REAP-30-abliterated

• the master for further surgery (re-abliteration, LoRA merge, fine-tune) and for making your own

quants. The upstream REAP parent is 0xSero/Qwen3.5-88B.

Provenance

Qwen3.5-122B-A10B (Apache-2.0) -> REAP-30 expert-prune (0xSero) -> abliterated bf16 (Robinson Labs)

-> Q8_0 master -> generic-imatrix GGUF quants.