license: apache-2.0
base_model: 0xSero/Qwen3.5-99B
library_name: transformers
pipeline_tag: text-generation
tags:
- abliterated
- qwen3.5
- moe
- reap
- not-for-all-audiences
Qwen3.5-122B-A10B-REAP-20 - Abliterated (bf16 base)
Abliterated bf16 safetensors base 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
in-house. This repo is the full-precision master, in safetensors.
This is the lighter-prune sibling of the REAP-30 base:
20% of experts removed instead of 30%, keeping more of the original capacity at a larger footprint.
This is the bf16 base that the
RobinsonLabs/Qwen3.5-122B-A10B-REAP-20-abliterated-GGUF
quant ladder was quantized from. If you want a ready-to-run quant, use that repo. This repo is the
master for further surgery (re-abliteration, LoRA merge, fine-tune) and for rolling your own quants.
Like the REAP-30 variant, this build has no Multi-Token Prediction (MTP / NextN). The real model
is a clean 48-layer qwen35moe - standard single-token prediction.
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.
Method
- Expert prune (REAP): ~20% of the MoE experts removed by 0xSero's REAP method, 122B -> ~99B
params (205 of 256 experts),qwen35moearch, A10B active budget retained. Done upstream, in
0xSero/Qwen3.5-99B. - 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. - Format: safetensors, sharded, with config + tokenizer + index. No vision tensors, no MTP head.
Files
| Format | Precision | ~Size | Notes |
|---|---|---|---|
| safetensors (50 shards) | bf16 | ~185 GB | abliterated REAP base; qwen35moe, 48 layers, no nextn |
The model is 48 transformer layers (block_count=48), qwen35moe architecture, ~99B params with an
A10B active-expert budget. The upstream config declares a phantom nextn (MTP) layer that carries
no weights - downstream GGUF converters should treat this as a clean 48-layer model
(block_count=48) and ignore the phantom head.
Quants
GGUF quants (Q6_K down to IQ2_XS, imatrix-weighted, 48-layer / no MTP) are published at
RobinsonLabs/Qwen3.5-122B-A10B-REAP-20-abliterated-GGUF.
Provenance
Qwen3.5-122B-A10B (Apache-2.0) -> REAP-20 expert-prune (0xSero, repo "Qwen3.5-99B") ->
abliterated bf16 (Robinson Labs). This safetensors repo is the abliterated bf16 master; the GGUF
ladder is quantized from it.