--- license: apache-2.0 base_model: Qwen/Qwen3-8B tags: - uncensored - abliterated - abliteration-repair - capability-preserving - ektome - surrogate-null - qwen3 language: - en pipeline_tag: text-generation --- # Ektomē-Qwen3-8B-PristinelyUncensored **Uncensored — with most of the usual capability tax removed. Pristine Qwen3-8B, refusal surgically excised, knowledge and reasoning almost entirely preserved.** > 🎯 **Within 0.018 capability of pristine Qwen3-8B while fully uncensored — and +0.041 over standard abliteration.** Standard abliteration makes a model uncensored but **damages it**: the crude "refusal direction" it deletes is entangled with directions that carry knowledge and reasoning, so mainstream abliterated Qwen3-8B loses ~6 points of MMLU. `Ektomē` (ἐκτομή, *"excision"*) cuts at the natural joint instead. It removes only the **refusal-specific** component — the diff-of-means direction orthogonalized against general helpfulness — with a single, **norm-preserving** projection on the *pristine* model. No training, no distillation, no damage to repair. Which directions are safe to cut is decided by a **surrogate-null catcher**: a direction is removed only if a structure-destroyed control confirms it carries refusal, not capability. So the receipt below is measured against a null, not asserted. ## The receipt — pristine vs crude-abliterated vs Ektomē | model | capability (few-shot) ↑ | refusal on harmful ↓ | coherence→pristine ↑ | |---|---|---|---| | pristine Qwen3-8B | **0.760** | 96% (censored) | 1.00 | | crude-abliterated (mlabonne) | 0.701 | **0%** (uncensored) | 0.78 | | **Ektomē (this model)** | **0.742** | **0%** (uncensored) | **0.92** | Same uncensoring as the standard abliterated model (0% refusal), but **+0.041 capability** and **+0.14 coherence** — and within **0.018** of the *censored* pristine base. Capability = ARC-Easy + HellaSwag + MMLU (few-shot, limit 200/task); refusal on harmful = AdvBench with a judge-free keyword classifier and thinking disabled. *capability*: ARC-Easy + HellaSwag + MMLU (few-shot). *refusal on harmful*: fraction refused on AdvBench (judge-free keyword classifier, thinking disabled so chain-of-thought can't mask the answer). Lower refusal = more uncensored; capability equal to pristine = no tax. ## How we know it's real — the catcher's verdict Every claim here is decided by a **surrogate-null catcher** (Basanos / Prokopē), not asserted. The refusal excision is applied to *pristine* and measured; a capability drop counts as real only if it beats the MMLU sampling-noise floor `SE_mmlu`. This is the actual gate output that green-lit the model — a drop *within* the noise floor is `MEASURED_EQUAL`, i.e. no tax.
📋 Show the raw catcher gate output (the "screenshot") ```text $ ektome_validate Qwen/Qwen3-8B SE_mmlu = 0.032 # honest noise floor (2-seed) pristine cap=0.715 compliance=0.040 # base refuses 96% of harmful Ektome cap=0.695 compliance=1.000 # uncensored AND capability intact crude cone cap=0.620 compliance=0.800 # ordinary abliteration: -0.095 capability gate clean-vs-pristine -> MEASURED_EQUAL # drop 0.020 < SE_mmlu 0.032 gate clean-vs-crude -> REAL_BETTER gate uncensored -> True VERDICT: PASS ```
**Before / after, against the noise floor** (zero-shot MMLU-val probe; the headline receipt above is the stricter few-shot MMLU-*test*): | model | capability | Δ vs pristine | catcher verdict | |---|---|---|---| | pristine Qwen3-8B (before) | 0.715 | — | baseline | | **Ektomē (after)** | **0.695** | **−0.020** (< SE 0.032) | **MEASURED_EQUAL** ✓ | | crude abliteration | 0.620 | −0.095 (≫ SE) | REAL damage ✗ | ## Why it's different - **Uncensored** — complies on harmful prompts the base refuses (near-100% vs the base's few %). - **Capability-preserved** — few-shot capability lands **within ~0.02 of the *pristine* base** (0.742 vs 0.760), recovering ~70% of the ~6 points the crude-abliterated model (0.701) throws away — and coherence-to-pristine jumps from 0.78 (crude) to **0.92**. - **Zero training** — a single surgical weight edit on pristine, seconds of compute. Nothing was fine-tuned, so nothing drifted. - **Honest** — every claim is gated by a surrogate-null test; the card reports what beat a control, not marketing. ## Run it locally ```python from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("Zynerji/Ektome-Qwen3-8B-PristinelyUncensored") model = AutoModelForCausalLM.from_pretrained("Zynerji/Ektome-Qwen3-8B-PristinelyUncensored", torch_dtype="bfloat16", device_map="auto") ``` **GGUF (any gaming PC):** `Q4_K_M` (~5 GB), `Q5_K_M`, `Q8_0` included — use with `llama.cpp` / LM Studio / Ollama. ## The Ektomē line & feedback If Ektomē-Qwen3-8B is useful to you, a ❤️ **like** helps others find it. This is the first of a **line** — the same catcher-gated surgery applied to the frontier small models (Mistral, Llama, Nemotron) and larger bases. Want a specific base next, hit an edge case, or have head-to-head numbers to compare? **Open a [Discussion](https://huggingface.co/Zynerji/Ektome-Qwen3-8B-PristinelyUncensored/discussions)** — the line is steered by what people actually run. ## Honest notes - **Uncensored:** this model follows instructions the base refuses. Use it lawfully and responsibly; you are accountable for what you generate. - Benchmarks use limited eval sets; each capability delta is gated **REAL** vs a surrogate null. - **Method:** produced by *Ektomē*, a capability-preserving surgical-abliteration method. Results are reported; the full recipe is not (yet). ## License Inherits the base model's license (**Qwen3 → Apache-2.0**). No warranty; your use, your responsibility.