---

license: mit

base_model: XiaomiMiMo/MiMo-V2.6-Flash-RL

tags:

- uncensored

- abliterated

- heretic

- gguf

- mimo

---

MiMo-V2.6-Flash-RL Uncensored Heretic — merged GGUF

> ⚠️ Content warning: This model 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 merged GGUF of

MiMo-V2.6-Flash-RL

(309B total / 15B active MoE, MIT license) decensored / "abliterated" 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. The ablation (trial 85 of study mimo26flash) was baked directly

into the

MXFP4 weights:

edited tensors were dequantized, patched with the exact ablation delta, and

requantized to their original type; everything else is a byte-for-byte copy.

This is the zero-runtime-overhead form — a drop-in base model, no

--lora flag needed. If you prefer the lossless option (bit-identical

base weights, ~70 MB download, requantization avoided entirely), the exact

same configuration is also available as a LoRA adapter at

MiMo-V2.6-Flash-RL-Uncensored-Heretic-LoRA-GGUF.

**heretic-gguf is available at

github.com/MoriNoNushi/heretic-gguf** —

the full tool, so the method can be applied to other GGUF models.

Results

Measured on 140 harmful prompts (100 from mlabonne/harmful_behaviors

test + 40 custom) and 100 harmless prompts (mlabonne/harmless_alpaca

test), CoT-skip prefix (<think></think>, thinking suppressed), greedy

decoding, 100-token responses, against the MXFP4 base:

| | Refusal rate (harmful) | KL divergence (harmless) |

|---|---|---|

| Base model | 95.71% (134/140) | 0 (by definition) |

| Ablated model (trial 85) | 3.57% (5/140) | 0.0568 |

The scores above were measured on the LoRA form of the ablation; the merged

model applies the identical delta, requantized to MXFP4/Q8_0, so behavior

should match within quantization noise. Refusals are counted by

refusal-keyword matching (English + Chinese + first-person-negation markers

such as "I'm not going to / able to ..."). KL divergence is measured on

first-token logits on harmless prompts. Note that the study was run with the

CoT-skip prefix (thinking suppressed, as in stock Heretic); with full

thinking enabled the model may still reason its way back to a refusal

mid-trace, so real-use refusal rates can be somewhat higher than the 3.57%

above.

> Note on KL: the KL divergence above (and the optimization objective

> itself) was measured against the MXFP4 quant. KL is a

> baseline-relative metric, so on a different base quant the effective drift

> from that quant's baseline may differ.

Usage

llama-server \
    -m MiMo-V2.6-Flash-RL-Uncensored-Heretic-MXFP4-00001-of-00002.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. MiMo-V2.6-Flash-RL (mimo2) support is merged

upstream in llama.cpp — any recent build works, no patches or PRs needed.

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) 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). This

model is trial 85 of study mimo26flash, exported with

heretic-gguf export --mode merged.

• Configuration (study mimo26flash, trial 85; global direction scope,

direction index 26.2 of 48; per-expert strengths scaled by measured

harmful/harmless routing frequency; row_normalization = "pre"):

- attn.o_proj: max weight 6.39 @ layer 36.4 of 48.

- routed MLP down-proj: max weight 1.58 @ layer 31.9.

• Merged export: heretic-gguf expresses ablation as a rank-1 LoRA

overlay (the same math stock Heretic writes into PEFT adapters); the

merged exporter materializes that delta exactly — full-rank, no LoRA

factorization loss — and requantizes only the patched tensors to their

original type (MXFP4 experts, Q8_0 attention/dense). This is one extra

quantization step on those tensors relative to the base; the

LoRA form

avoids it entirely.

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 the 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 the model produces

and for any consequences of using it.** The authors of this release, of

heretic-gguf, of Heretic, and of Xiaomi 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.