license: mit
base_model: zai-org/GLM-5.3-Flash
tags:

• uncensored
• abliterated
• heretic
• lora
• gguf
• glm

GLM-5.3-Flash Heretic — LoRA adapter V2

⚠️ Content warning: This adapter has had the base model's refusal
behavior surgically suppressed. The resulting model 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 rank-1 LoRA adapter that decensors / "abliterates"
GLM-5.3-Flash
(320B total / 18B 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.

What's new in V2. The V1 adapter was optimized with a CoT-skip
evaluation (thinking suppressed) — and its winners still refused in real
use, where GLM-5.3-Flash always thinks first and can reason its way
back into a refusal mid-trace. V2 comes from a study that optimizes the
path that actually matters: full thinking enabled (reasoning effort
high), refusal scored on the final answer only. The refusal directions
were also recomputed with winsorization (5% outlier trim) at the
pre-thinking residual position, which cut the KL cost of strong ablation
roughly 4×, and the search was rebalanced toward KL-free MLP/shared-expert
ablation strength. The study is ongoing; this adapter is trial 61 of
study glm53think2.

This repository contains only the adapter. You need the base model
separately — any GGUF quant of GLM-5.3-Flash works, since the adapter is
applied in f32/f16 compute regardless of the base quant (it was tuned and
evaluated against
UD-IQ4_XS). The LoRA
form is the lossless option: the base weights are never modified or
requantized, and the download is ~86 MB instead of ~160 GB.

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

Results

Without thinking (comparable to the V1 card)

Measured exactly as the V1 release: 140 harmful prompts (100 from
mlabonne/harmful_behaviors test + 40 custom) and 100 harmless prompts
(mlabonne/harmless_alpaca test), CoT-skip prefix, greedy decoding,
100-token responses, against the UD-IQ4_XS base:

(V1 measured 26.43% / 0.0682 on this same benchmark — V2 trades a little
KL for a ~2.6× lower refusal rate.)

With thinking enabled (how the model is actually used)

Reasoning effort high, full thinking traces (up to 1280 tokens),
refusal scored on the final answer after </think>:

The thinking numbers are higher because the model can re-derive a refusal
inside its reasoning trace — that is precisely the behavior this study
optimizes against, and these are the scores the adapter was selected on.
Refusals are counted by refusal-keyword matching (English + Chinese +
first-person-negation markers such as "I'm not going to / able to ...",
GLM-5.3's dominant refusal phrasing). KL divergence is measured on
first-token logits on harmless prompts.

Note on KL: the KL divergence above (and the optimization objective
itself) was measured against the UD-IQ4_XS quant. KL is a
baseline-relative metric, so if you run the adapter on a different quant,
the effective drift from that quant's baseline may differ.

Usage

llama-server \
    -m GLM-5.3-Flash-UD-IQ4_XS-00001-of-00005.gguf \
    --lora glm-5.3-heretic-lora-v5.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. Simply omitting --lora restores the base model
exactly.

llama.cpp requirements. GLM-5.3-Flash (glm5next) support had not
landed on llama.cpp master at the time of this release — use a build of
PR #27754 (or master,
once merged). In addition, the stock PR routes the KDA layers' attention
out-projection through a raw ggml_mul_mat, which silently ignores LoRA on
those tensors — 31 of the 45 layers. The adapter was tuned and evaluated
with a one-line patch that fixes this
(llama_cpp_glm5next_lora.patch, shipped in the
heretic-gguf repo); apply it
for the adapter's attention ablation to take full effect. Without the patch
the adapter still applies to the MLP experts and the MLA layers, but its
effect is weaker than the measured numbers above.

How it was made

• Method: directional ablation ("abliteration") — the refusal direction
  in residual space (difference of means over 480 harmful / 480 harmless
  prompts, 5% winsorized, orthogonalized against the harmless mean,
  captured at the pre-thinking position with reasoning effort high) is
  projected out of the attention output and MoE down-projection weights.
  Strengths, layer kernels, and direction selection were tuned by
  multi-objective Optuna TPE (minimize refusal rate and KL jointly) under
  a thinking-enabled evaluation. The study is ongoing; this adapter is
  trial 61 of study glm53think2.
• Configuration (study glm53think2, trial 61; per-layer direction
  scope; per-expert strengths scaled by measured harmful/harmless routing
  frequency; row_normalization = "pre"):
  • attn.o_proj: max weight 6.49 @ layer 27.4 of 45.
  • routed MLP down-proj: max weight 2.24 @ layer 34.7.
  • shared-expert down-proj: max weight 5.09 @ layer 31.5.
• Why a LoRA: heretic-gguf expresses ablation as a rank-1 LoRA overlay,
  the same math stock Heretic writes into PEFT adapters. Shipping the
  adapter avoids requantizing the ~160 GB base entirely — bit-identical base
  weights, instant to apply. (A merged export of the IQ4_XS quant is also
  technically impossible — imatrix quants have no requantizer — so the
  adapter is the only lossless way to ship this configuration.) The adapter
  embeds its full provenance (study, trial, parameters, scores, commit
  hashes) as adapter.heretic.* GGUF metadata keys; inspect with
  strings glm-5.3-heretic-lora-v5.gguf | grep adapter.heretic.

Responsible use & disclaimer

• This adapter can make the base model 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 the model says should be treated as accurate, safe, or legal
  advice.
• Do not deploy models using this adapter 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 the model produces
  and for any consequences of using this adapter. The authors of this
  release, of heretic-gguf, of Heretic, of Unsloth, and of Z.ai accept
  no liability whatsoever. Using this adapter 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 adapter you acknowledge the above.

License

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