A baked refusal-ablation (abliteration) of XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B (BF16, 9.41B).
The refusal direction is written into the weights themselves: only 12 MLP tensors at layers 16–19 are edited (rank-1 directional ablation, output side W ← W − 6.0·u(uᵀW), input side W ← W − 1.0·(Wu)uᵀ), plus per-layer surface-policy attenuation, a 128-neuron deflection mask on mlp.down_proj, and a small lm_head term. The other 748 of 760 tensors are bit-identical to the parent (full-tensor verified).
All numbers below were measured from disk with the released checkpoint loaded separately from its parent (greedy, thinking OFF unless noted). Parent measured in the same harness:
| Metric | Parent | This model | Guard |
|---|---|---|---|
| Refusal-phrase removal (paired, n=218) | 0/218 | 216/218 | ≈216/218 |
| Hidden refusal holdout (n=46) | — | 45/46, benign reverse-flip 0 | 45/46, flip 0 |
| GSM8K (n=200) | 182 | 175 | ≥173 |
| MMLU (n=200) | 124 | 122 | ≥119 |
| ΔNLL (fixed 240-doc probe) | 0 | +0.2613 nats/token | ≤+0.43 |
| Tool-call format / exact (BFCL-style, n=32) | 0.906 / 0.281 | 0.906 / 0.281 | ≥0.906 / ≥0.250 |
| Delivery / refusal-of-doctrine (style judge, in-pass) | 27.9 / 34.0 | same pass controls | ≥28 / ≤35 |
Refusal phrasing is removed across languages: English, Japanese (12/12), and a 46-prompt hidden holdout, with zero new refusals on benign prompts.
A manual audit of 44 randomly sampled "removed" refusals found:
| Outcome of a removed refusal | Share |
|---|---|
| Substantive answer (actually delivers the requested content) | 10–14% |
| Soft deflection (defensive framing, safe alternative, substitute topic) | ~75% |
| Off-topic / incoherent | ~7% |
The refusal phrasing is gone; refusal behavior is mostly converted into deflection, not compliance. Benchmark suites (GSM8K/MMLU/tool-calls) do not see the ~7% derailment axis — it shows up mainly on harmful-looking inputs. Treat downstream evaluations with the same skepticism.
Also unmeasured: thinking mode ON, image input. MTP weights do not exist in the parent.
The baked edit itself is format-robust: it survives Q4_0 / Q4_K_M / NVFP4 PTQ with removal intact (paired ≥216/218, benign reverse-flip 0). However no 4-bit PTQ format passes the full capability guard set (MMLU / GSM8K / tool-call format all degrade to varying degrees; NVFP4 degrades hardest). Ship BF16, or quantize knowing the trade-off.
Quantized derivatives are not released yet — this repo contains only the verified BF16 checkpoint:
Nothing unverified will be uploaded here; each derivative will ship with its own measured guard table when it earns one.
Arditi et al. (2024)-style single-direction refusal ablation, applied weight-side. Direction extracted as the difference of mean last-prompt-token activations (refused − harmless), per layer. Dose and layer band (L16–19) were selected by sweep against paired removal, held-out capability probes, and ΔNLL — full recipe, per-tensor operation log, and SHA256 provenance of every edited tensor are in abliteration_meta.json.
Random-direction and wrong-writer controls (attention o_proj, linear_attention.out_proj, community-standard all-writer α=1) were measured and rejected: they either do nothing or collapse capability (GSM8K/MMLU 0/100). Only this recipe passes.
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"Hikari07jp/MiMo-V2.6-Distill-Qwen-9B-Ablitrated",
torch_dtype="bfloat16",
device_map="auto",
)
tok = AutoTokenizer.from_pretrained("Hikari07jp/MiMo-V2.6-Distill-Qwen-9B-Ablitrated")
For serving, the parent's SGLang instructions apply unchanged (see parent model card); no reasoning-parser or template changes are needed.
model-00002-of-00006.safetensors 4.0 GBmodel-00004-of-00006.safetensors 4.0 GBmodel-00003-of-00006.safetensors 4.0 GBmodel-00005-of-00006.safetensors 4.0 GBmodel-00001-of-00006.safetensors 2.0 GBmodel-00006-of-00006.safetensors 800 MB