Abliterated, importance-matrix (imatrix) quantized GGUFs of 0xSero/Qwen3.5-99B - 0xSero's ~20% MoE expert-prune (REAP) of Qwen/Qwen3.5-122B-A10B, taking the model from 122B down to ~99B parameters (205 of 256 experts kept) while keeping the A10B active-expert budget and the qwen35moe architecture. Robinson Labs then abliterated the pruned model and quantized it here.

This is the lighter-prune sibling of the REAP-30 build: 20% of experts removed instead of 30%, so it keeps more of the original model's capacity at a larger footprint. Like the REAP-30 variant it 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-20-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. 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-20-abliterated-Q6_K.gguf`	Q6_K	81.3 GB	near-lossless
`Qwen3.5-122B-A10B-REAP-20-abliterated-Q5_K_M.gguf`	Q5_K_M	70.4 GB
`Qwen3.5-122B-A10B-REAP-20-abliterated-Q4_K_M.gguf`	Q4_K_M	60.2 GB
`Qwen3.5-122B-A10B-REAP-20-abliterated-Q4_K_S.gguf`	Q4_K_S	56.5 GB
`Qwen3.5-122B-A10B-REAP-20-abliterated-IQ4_XS.gguf`	IQ4_XS	53.2 GB	quality/size sweet spot
`Qwen3.5-122B-A10B-REAP-20-abliterated-Q3_K_M.gguf`	Q3_K_M	47.6 GB
`Qwen3.5-122B-A10B-REAP-20-abliterated-IQ3_M.gguf`	IQ3_M	43.7 GB
`Qwen3.5-122B-A10B-REAP-20-abliterated-IQ3_XS.gguf`	IQ3_XS	40.9 GB
`Qwen3.5-122B-A10B-REAP-20-abliterated-IQ2_M.gguf`	IQ2_M	32.8 GB
`Qwen3.5-122B-A10B-REAP-20-abliterated-IQ2_XS.gguf`	IQ2_XS	29.5 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): ~20% of the MoE experts removed by 0xSero's REAP method, 122B -> ~99B params (205 of 256 experts), 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 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-20-abliterated

the master for further surgery (re-abliteration, LoRA merge, fine-tune) and for making your own quants. The upstream REAP parent is 0xSero/Qwen3.5-99B.

Provenance

Qwen3.5-122B-A10B (Apache-2.0) -> REAP-20 expert-prune (0xSero) -> abliterated bf16 (Robinson Labs) -> generic-imatrix GGUF quants.

RobinsonLabs/Qwen3.5-122B-A10B-REAP-20-abliterated-GGUF

Qwen3.5-122B-A10B-REAP-20 - Abliterated GGUF

Disclosure

Files

Method

bf16 base

Provenance

RobinsonLabs/
Qwen3.5-122B-A10B-REAP-20-abliterated-GGUF