---

license: apache-2.0

base_model: OpenMOSE/Qwen3.5-REAP-262B-A17B

library_name: gguf

pipeline_tag: text-generation

tags:

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

---

Qwen3.5-REAP-262B-A17B - Abliterated GGUF

Abliterated GGUF quant ladder of

OpenMOSE/Qwen3.5-REAP-262B-A17B,

itself a 34% REAP expert-pruning of Qwen3.5-397B-A17B down to

262B total / ~17B active. Every rung is importance-matrix (imatrix) weighted.

> Rebuilt 2026-09 (native-capture). The rungs published here supersede an earlier build that

> under-abliterated because it reused a refusal direction captured from the 212B sibling instead of

> capturing one from this model. This build captures the direction natively from the 262B and

> applies it per layer. See Build history below. Thanks to community tester azwarners for the

> reproducible report that pinned it down.

The full-precision bf16 safetensors master lives at

RobinsonLabs/Qwen3.5-REAP-262B-A17B-abliterated,

use that repo if you want to re-abliterate, merge a LoRA, fine-tune, or roll your own quants.

Disclosure

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

via per-layer weight orthogonalization along a captured refusal direction. **Child-safety guardrails

are retained by design and were measured intact** (see Evaluation), self-harm prompts still redirect

to help (e.g. 988), and it is not intended to assist genuine wrongdoing. Capability is preserved.

Tagged not-for-all-audiences. Use responsibly, you are responsible for what you generate with it.

License inherited from the base model: Apache-2.0.

Evaluation

Measured on this D51H build (the abliterated model these rungs are cut from):

| Axis | Result |

|---|---|

| Refusal on a 25-prompt held-out harmful set | 1/25 (4%) complied-vs-refused, i.e. abliteration effective |

| Capability (reasoning, code, math, factual recall, instruction-following) | 5/5 intact |

| Child-safety guardrail (held-out subset, LLM-judged) | 10/10 still refused (kept by design) |

The refusal and guardrail axes are judged by an LLM rather than keyword-matched, so a principled

deflection counts as a refusal. The IQ3_XS rung was separately smoke-tested and confirmed to

retain the abliteration at 3.28 bpw. Very low rungs (IQ2 family) trade fidelity for size in the

usual way, evaluate for your use case.

Files

| File | Quant | bpw | ~Size | imatrix | Notes |

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

| ...-Q5_K_M.gguf | Q5_K_M | 5.68 | ~186 GB | yes | highest-fidelity rung published |

| ...-Q4_K_M.gguf | Q4_K_M | 4.85 | ~159 GB | yes | K-quant quality pick |

| ...-Q4_K_S.gguf | Q4_K_S | 4.55 | ~149 GB | yes | |

| ...-IQ4_XS.gguf | IQ4_XS | 4.28 | ~140 GB | yes | quality/size sweet spot |

| ...-Q3_K_M.gguf | Q3_K_M | 3.83 | ~125 GB | yes | |

| ...-IQ3_M.gguf | IQ3_M | 3.51 | ~115 GB | yes | |

| ...-IQ3_XS.gguf | IQ3_XS | 3.29 | ~107 GB | yes | runs on a 4-bit-context AI PC |

| ...-IQ2_M.gguf | IQ2_M | 2.62 | ~86 GB | yes | |

| ...-IQ2_XS.gguf | IQ2_XS | 2.36 | ~77 GB | yes | |

| ...-IQ2_XXS.gguf | IQ2_XXS | 2.12 | ~69 GB | yes | smallest |

bpw figures are as reported by llama-quantize, not nominal. Every rung, K and IQ alike, is

imatrix-weighted. The IQ rungs land meaningfully smaller than the K-quant of comparable quality:

IQ4_XS undercuts Q4_K_S by ~9 GB, and the IQ2 family is the only path under 90 GB.

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

Architecture notes

qwen3_5_moe hybrid: 60 decoder layers (45 linear-attn / DeltaNet + 15 full-attn,

full_attention_interval=4), 333 experts with 10 active per token, hidden size 4096,

head dim 256, 262144 native context. No MTP / NextN layer. This is the text path only

(no vision mmproj).

The high expert count is the defining feature of this REAP tier: 333 experts versus 267 on the

48%-pruned 212B sibling.

More experts retained means more of the 397B parent's routing diversity survives, at the cost of size.

Method

• Abliteration: per-layer weight orthogonalization. For every matrix that writes the residual

stream (o_proj, DeltaNet out_proj, fused expert down_proj, shared-expert down_proj), the

rank-1 component along that layer's refusal direction is subtracted, with a per-layer weight and a

layer search rather than one global scalar. Routers and norms pass through byte-identical.

• Refusal direction, captured natively from this model. The direction is captured from the

262B's own trunk activations (not borrowed from a sibling), and guarded against attention-sink

contamination: the sink dimensions that dominate raw activation magnitude (and would brick the

model if ablated) are down-weighted so the edit lands on the clean, spread-out consensus of the

attention path.

• Quant: convert to bf16 GGUF and quantize with llama.cpp.

Every rung is cut directly from the bf16 master (the IQ rungs with --allow-requantize semantics

handled by the imatrix pass), not from a lossy intermediate.

• imatrix: rungs are weighted by an importance matrix over an rldomain calibration corpus. The

high chunk count is deliberate: with 333 experts, a short calibration run leaves rarely-routed

experts under-exercised, so per-tensor coverage needs many more chunks than a dense model to

saturate.

Build history

• 2026-09 (current, native-capture). Refusal direction captured from the 262B itself, per-layer

orthogonalization with a layer search and an attention-sink guard. Evaluated as above. These are

the files in this repo.

• 2026-07 (superseded). The first build reused the refusal direction captured from the 212B

sibling; on the larger model that under-abliterated, so some adult/creative prompts still refused

(discussion #1).

Every rung has been recut in place with the native-capture direction. If you pulled before 2026-09,

re-download.

bf16 base

The full-precision bf16 safetensors master this ladder derives from is at

RobinsonLabs/Qwen3.5-REAP-262B-A17B-abliterated.

That repo is the master for further surgery (re-abliteration, LoRA merge, fine-tune) and for making

your own quants.

Provenance

Qwen3.5-397B-A17B (Apache-2.0) -> OpenMOSE/Qwen3.5-REAP-262B-A17B (34% REAP prune) ->

abliterated (bf16 master, native 262B direction capture) -> imatrix-weighted quant rungs cut

from the bf16 master. Recipe and diagnosis are Robinson Labs internal (WI #1423 / #3406).

Built by Robinson Labs.