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MorinoNushi/
DeepSeek-V4-Flash-0731-heretic-GGUF

Name: DeepSeek-V4-Flash-0731-heretic-GGUF
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deepseek-ai/DeepSeek-V4-Flash-0731

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DeepSeek-V4-Flash-0731 Heretic — merged GGUF (UD-Q8_K_XL)

⚠️ Content warning: This model has had its refusal behavior surgically suppressed. It will comply with requests the base model refuses, including requests that are harmful, unethical, offensive, or illegal. It has reduced safety guardrails. See Responsible use below — you are solely responsible for what you do with it.

This is a decensored / "abliterated" variant of DeepSeek-V4-Flash-0731 (284B total / 13B active MoE, MIT license), produced with heretic-gguf — a GGUF-native port of Heretic's Optuna-optimized directional ablation, which runs the whole search directly on quantized GGUF weights via llama.cpp.

This repository contains the merged, ready-to-run model (UD-Q8_K_XL, 5 shards): the ablation is baked directly into the weights, no adapter needed. It was produced by copy-and-patch from the official Unsloth UD-Q8_K_XL — every tensor except the ablated ones is byte-identical to the base; the edited tensors went through one extra requantization step (a small, tensor-local rounding error). If you want the lossless option, a rank-1 LoRA adapter GGUF with the identical ablation is published at MoriNoNushi/DeepSeek-V4-Flash-0731-heretic-GGUF-lora — apply it on top of any base quant with llama-server --lora.

heretic-gguf is available at github.com/MoriNoNushi/heretic-gguf — the full tool, so the method can be applied to other GGUF models.

In initial hands-on testing the abliterated model shows excellent general capabilities — responses remain coherent, detailed on normal tasks — and it has not refused a prompt during personal testing. (Anecdotal, not a benchmark; see the measured numbers below.)

Results

Measured on a held-out eval set of 140 harmful prompts (100 from mlabonne/harmful_behaviors test + 40 custom) and 100 harmless prompts (mlabonne/harmless_alpaca test), greedy decoding, 100-token responses, against the UD-Q8_K_XL base:

	Refusal rate (harmful prompts)	KL divergence (harmless prompts)
Base model	99.29% (139/140)	0 (by definition)
This model	14.29% (20/140)	0.0569

Refusals are counted by refusal-keyword matching (English + Chinese markers); KL divergence is measured on first-token logits on harmless prompts, so lower = less collateral damage to normal behavior. This configuration was the Pareto-optimal point of a 387-trial Optuna study: the only trial with fewer refusals cost KL 0.089 (57% more drift), and the only trial tying its refusal rate had strictly higher KL. (Scores were measured on the lossless adapter overlay; the merged weights implement the identical ablation, up to the one requantization step described above.)

Usage

bash

llama-server \
    -m DeepSeek-V4-Flash-0731-heretic-UD-Q8_K_XL-00001-of-00005.gguf \
    --jinja

Add your usual offload/context flags (-ngl 999, -c, tensor splits, etc.) — nothing model-specific is required, and no special sampling parameters are needed.

How it was made

Method: directional ablation ("abliteration") — the refusal direction in residual space (difference of means over 480 harmful / 480 harmless prompts, orthogonalized against the harmless mean) is projected out of the attention output and MoE down-projection weights. Strengths, layer kernel, and direction index were tuned by multi-objective Optuna TPE (minimize refusal rate and KL jointly) across a base study and three seeded follow-up studies — 1,822 trials total (514 + 513 + 408 + 387).
Winning configuration (study followup3, trial 25): global direction scope, direction index 21.05 of 43 layers; attn max weight 4.49 @ layer 29; routed-expert MLP max weight 1.06 (per-expert strengths scaled by measured harmful/harmless routing frequency); shared-expert weight 0.48.
Why merged: heretic-gguf expresses ablation as a rank-1 LoRA overlay (the same math stock Heretic writes into PEFT adapters); for this release the winning overlay was merged into the weights by streaming the base shards, dequantizing only the ablated tensors, applying the exact full-rank delta, and requantizing to the original tensor types. Convenient single artifact at the cost of one extra quantization step on the edited tensors.

Responsible use & disclaimer

This model can generate content that is offensive, disturbing, hateful, sexually explicit, violent, or otherwise objectionable, including detailed instructions for harmful or illegal acts. That is the direct and intended consequence of removing refusal behavior.
The ablation suppresses refusals, not the base model's knowledge — outputs on dangerous topics may be wrong, hallucinated, or incoherent. Nothing this model says should be treated as accurate, safe, or legal advice.
Do not deploy this model in any production system, public-facing service, or multi-user setting. It is intended for personal research, red-teaming, and evaluation purposes.
You, the user, are solely responsible for any output this model produces and for any consequences of using it. The authors of this release, of heretic-gguf, of Heretic, of Unsloth, and of DeepSeek accept no liability whatsoever. Using this model to produce illegal content or to harm others is your choice and your legal exposure — ensure your use complies with all applicable laws in your jurisdiction.
By downloading or using this model you acknowledge the above.

License

The base model is MIT-licensed (see the base repo); this model inherits those terms. The heretic-gguf tooling used to produce it is AGPL-3.0-or-later.

MorinoNushi/DeepSeek-V4-Flash-0731-heretic-GGUF