Qwen3.8-27B RVN Heretic Abliterated Uncensored (GGUF)

RVN is a double-refined abliterated variant of Qwen3.8-27B, built on top of trohrbaugh/Qwen3.8-27B-heretic-ara (an ARA abliteration by Tim Rohrbaugh) and further refined with two additional full-weight ARA passes targeting residual refusals. It retains very low behavioral damage (KL ≈ 0.0085) while reducing harmful-prompt refusals from 3/100 (source) to 0–1/100 in independent measurements.

Note on this repository's history. This repo previously hosted the original Qwen3.8-27B-Heretic-Q4_K_M.gguf (single-quant release from the earlier trohrbaugh/Qwen3.8-27B-heretic source). That file is kept as legacy for download-count continuity and backward compatibility — it is the older abliteration variant and is superseded by the RVN files below. Prefer the RVN quants for new deployments.

Not for all audiences. This model has reduced safety guardrails by design. It is intended for adult audiences (18+) doing research, creative writing, roleplay, and uncensored generation. Certain guardrails are intentionally left in place; use responsibly and in accordance with your local laws.

What is ARA?

ARA (Arbitrary-Rank Ablation) is the abliteration technique implemented in p-e-w/heretic. Traditional directional abliteration finds a single "refusal direction" in activation space and subtracts it — a one-shot, low-rank surgery that is simple but can leave residual refusals or damage unrelated behavior.

ARA instead treats abliteration as a matrix optimization problem. For every target module (attention out-projection and MLP down-projection), it collects activations on "good" prompts (harmless requests) and "bad" prompts (harmful requests), then uses an LBFGS optimizer to rewrite the module's weight matrix so that:

• Preserve: outputs on good prompts change as little as possible (KL is kept low)
• Steer: outputs on bad prompts are pulled toward the good-prompt output manifold (via k-nearest-neighbor distances), so harmful requests stop triggering the refusal circuitry
• Overcorrect: outputs on bad prompts are additionally pushed away from the original bad-prompt outputs, which helps overcome complex, multi-stage refusal mechanisms

Because the weight matrix is optimized directly (rather than subtracting a single direction), ARA is "arbitrary rank" — it can carve out a much richer refusal-removal subspace while keeping behavioral damage minimal.

Why "Heretic" and "Abliterated"?

These two words describe two layers of the same process:

• Heretic is the tool: the open-source implementation of ARA (and related abliteration methods) used to modify the model. Models produced with it are commonly labeled "heretic" in the community.
• Abliterated is the result: the model's refusal behavior has been surgically removed. An abliterated model still knows everything the base model knows, but it no longer refuses to answer the categories that were steered away during the process.

So "Heretic Abliterated" means: abliterated using the heretic toolset. RVN goes one step further — it applies the ARA procedure three times total: once by the original author (trohrbaugh) to get from base Qwen3.8-27B to -ara, and twice more by us to get from -ara to RVN, squeezing out the last residual refusals.

Special Thanks

This work would not exist without Tim Rohrbaugh (trohrbaugh), whose heretic-ara ARA abliteration of Qwen3.8-27B (refusals 3/100, KL 0.0535) provided the foundation we refined into RVN. His upstream contributions to the heretic codebase — including the row-norm preservation feature and Qwen3.5 MoE/DeltaNet hybrid handling — are directly responsible for making DeltaNet-layer abliteration work at all. Thank you, Tim.

Model Overview

Why RVN?

trohrbaugh/Qwen3.8-27B-heretic-ara is already a strong ARA abliteration, but three harmful prompts still triggered refusals in our independent evaluation (racism website, malware, government database hacking). RVN applies two additional full-weight ARA passes using the same tight parameter set (start 26, end 56, preserve 0.9432, steer 0.0009, overcorrect 0.5038, neighbor 10), which:

• Reduced refusals from 3/100 → 0–1/100 (the only remaining refusal is a chemical-weapon WMD prompt — one of the strongest safety-trained categories, and intentionally one of the guardrails we left in place)
• Reduced KL damage from 0.0535 (source) to 0.0085 vs base — a ~6× improvement in behavioral preservation
• Verified independently on two rented GPU machines with prefix-based (real-answer) refusal measurement

Refusal evaluation (100 harmful-behaviors prompts, prefix-forced real answers)

Files & Quantization Spectrum

imatrix-based quants are produced from the same F16 with an activation importance matrix computed over wikitext-2-raw (original spectrum, 563 chunks) / tiny_shakespeare (2026-08-17 re-quant additions: IQ3_M fix + IQ2_S/IQ3_XXS/IQ3_XS/IQ3_S, llama-imatrix, -ngl 99).

Quant → GPU / Memory Guide

Sizes in the file table are the actual file sizes on the Hub (decimal GB / GiB), pulled from repository metadata. Full GPU load means the whole quant fits in VRAM; quants whose file size exceeds your VRAM need partial offloading.

2026-08-17 — RVN-IQ3_M incident & fix: the original RVN-IQ3_M.gguf generated only / characters on every backend (confirmed by the community and reproduced locally). A tensor-level audit showed corrupted quantization data — NaN/Inf block scales and fully zeroed tensors (e.g. token_embd had ~39.6M NaN values) — from a bad quantize run, not a llama.cpp regression (all other quants from the same F16 dequantize cleanly). The file was pulled, re-quantized from the F16 with a freshly computed imatrix, generation-tested ("The capital of France is" → Paris, 70+ t/s) and re-uploaded. New quants added the same day: IQ2_S, IQ3_XXS, IQ3_XS, IQ3_S, Q3_K_L, Q5_K_S.

KV cache math (GQA, 4 KV heads, head_dim 256): 2 × 64 layers × 4 KV heads × 256 head_dim × 2 bytes = 256 KiB/token FP16 → 16K ctx ≈ 4.2 GB · 32K ctx ≈ 8.4 GB · 64K ctx ≈ 16.8 GB (Q8_0 KV halves this). A 16 GB card running Q3_K_M (13.30 GB model) + 16K ctx Q8_0 KV fits comfortably; Q4_K_M (16.55 GB) really needs a 24 GB card.

Rule of thumb: pick the largest quant that leaves ≥ 4 GB for KV cache + compute buffers. If you only need short replies, drop the quant one notch and get a bigger context; if you need long context, prioritize KV budget over quant size.

Limitations & Responsible Use

• Reduced safety guardrails by design. This model is not intended for use in applications requiring robust safety filtering, content moderation, or deployment to minors.
• Certain guardrails are intentionally left in place. Abliteration targets refusal behavior on general harmful-prompt categories; a small set of hard safety-trained categories is deliberately not fully removed. Behavior may vary across domains and languages.
• Not affiliated with or endorsed by Qwen/Alibaba or trohrbaugh.

License & Attribution

• Base model: Qwen/Qwen3.8-27B — Apache-2.0
• Abliteration source: trohrbaugh/Qwen3.8-27B-heretic-ara — Apache-2.0
• This repo: Apache-2.0. See LICENSE.

Citation

@misc{rohrbaugh2026heretic,
  title={Qwen3.8-27B-heretic-ara: ARA Abliteration of Qwen3.8-27B},
  author={Rohrbaugh, Tim},
  year={2026},
  howpublished={\url{https://huggingface.co/trohrbaugh/Qwen3.8-27B-heretic-ara}}
}

@misc{rvn2026,
  title={RVN: Qwen3.8-27B Heretic Abliterated Uncensored},
  author={0bserverx},
  year={2026},
  howpublished={\url{https://huggingface.co/0bserverx/Qwen3.8-27B-Heretic-Abliterated-Uncensored-GGUF}}
}