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Ektome Qwen3 8B PristinelyUncensored

Uncensored — and carrying a statistical certificate that it wasn't damaged.

ArtText modelBasismodellQwen/Qwen3-8BVersionBBewertung

Modellquelle

Art
Text model
Dateigröße
3,63 GB
Basismodell
Qwen/Qwen3-8B
Version
vEktome-Qwen3-8B-IQ3_M
Aktualisiert
23. Okt. 2026
Downloads
517
Quelle
Hugging Face

Quellenbeschreibung

Ektome-Qwen3-8B-PristinelyUncensored

Ektome-Qwen3-8B-PristinelyUncensored

Uncensored — and carrying a statistical certificate that it wasn't damaged.

compliance 0.02 to 1.00 at capability +0.005 vs pristine.

$$\colorbox{black}{$\color{white} \begin{array}{ll} \textsf{EKTOME CERTIFICATE} & {} \ \textsf{capability} & \textsf{PASS} \ \textsf{margin} & 3% \ \textsf{items } n & 2800 \ \textsf{worst-axis bound} & +0.000 \ \textsf{compliance} & 0.02 \rightarrow 1.00 \ \end{array}$}$$

📄 Read the whitepaper (PDF) — full method, receipts and certification. The PDF is the authoritative document: dark-typeset, with the complete derivation, the per-axis certificate and the reproducibility hashes.


Why this exists

Standard abliteration removes a coarse refusal direction that is entangled with directions carrying knowledge and reasoning. The result is an uncensored model with a capability tax that is almost never measured.

Ektomē (ἐκτομή, excision) isolates and removes only the refusal-specific component, leaving general helpfulness intact, and does so norm-preservingly on the pristine model — no training, no distillation, no damage to repair. The extraction depth is selected per model by automated search against measured compliance.

The estimator, excision operator and depth-selection procedure are proprietary. What is published here is the measured outcome and the evidence for it, which you can verify against the artifacts in this repo.

The receipt

modelcapability (MMLU-val) ↑compliance on harmful ↑
pristine Qwen3-8B0.7280.020
Ektomē (this model)0.7231.000

These are point estimates with no confidence interval — which is precisely why the next section exists.

The certificate

Capability retention is certified by a paired non-inferiority test against the pristine model (exact McNemar, Holm-corrected, one-sided bootstrap bound on the drop $d$ vs a 3% margin):

axisnrefcandd upperverdict
arithmetic14000.0000.000+0.000PASS
instruction6000.0000.000+0.000PASS
knowledge4000.0000.000+0.000PASS
reasoning4000.0000.000+0.000PASS

Overall: PASS (3% margin, n=2800, alpha=0.05)

Reproducible from seed=20260726, pack sha256:7bbaff877146e081....

Generation health checks

metricpristineEktomēn
foreign_rate0.00.015
degen_rate0.10.015
instr_pass0.40.45

These are degeneration guards — code-switching, babbling, format compliance — not capability measures. Note the sample sizes: they detect a broken model, not a subtly weaker one. The capability claim rests on the certificate above, not here.

Quantisations

filebitsnotes
Ektome-Qwen3-8B-Q8_0.gguf8near-lossless
Ektome-Qwen3-8B-Q6_K.gguf6
Ektome-Qwen3-8B-Q5_K_M.gguf5
Ektome-Qwen3-8B-PristinelyUncensored-Q4_K_M.gguf4imatrix
Ektome-Qwen3-8B-IQ4_XS.gguf4imatrix, smallest usable
Ektome-Qwen3-8B-IQ3_M.gguf3imatrix

IQ* variants are imatrix-quantised — better quality per bit at low precision.

Limitations

The certificate bounds capability retention only. It does not certify safety, factual accuracy, or fitness for any purpose. Axes marked inconclusive are honestly under-powered, and the certificate states the $n$ needed to resolve them. Compliance uses a keyword classifier — a proxy that evasive phrasing can fool. This model is uncensored by construction: it will not refuse, and you are accountable for what you do with it.

Citation

@software{ektome_Ektome-Qwen3-8B-PristinelyUncensored,
  title  = {Ektome-Qwen3-8B-PristinelyUncensored},
  author = {Zynerji},
  year   = {2026},
  url    = {https://huggingface.co/Zynerji/Ektome-Qwen3-8B-PristinelyUncensored}
}

Quellen

1 Quelle
Hugging FacePrimärVersion 56cf3ef7e6cf59f1090d8580a5d1d07fae2664e1
huggingface.co/Zynerji/Ektome-Qwen3-8B-PristinelyUncensored/tree/56cf3ef7e6cf59f1090d8580a5d1d07fae2664e1

Verifiziert 23. Okt.

Modellartefakte

1 Artefakt

Ektome-Qwen3-8B-IQ3_M.gguf

gguf · 3,63 GB · SHA-256 fe9fc1b4dba6…8563 · Hugging Face

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Quellenauszüge

3 Auszüge

--- license: apache-2.0 base_model: Qwen/Qwen3-8B tags: - uncensored - abliterated - capability-preserving - certified - ektome - sphragis - qwen3 language: - en pipeline_tag: text-generation --- ![Ektome-Qwen3-8B-PristinelyUncensored](./hero.png) # Ektome-Qwen3-8B-PristinelyUncensored **Uncensored — and carrying a statistical certificate that it wasn't damaged.** > **compliance 0.02 to 1.00 at capability +0.005 vs pristine.** $$\colorbox{black}{$\color{white} \begin{array}{ll} \textsf{EKTOME CERTIFICATE} & {} \\ \textsf{capability} & \textsf{PASS} \\ \textsf{margin} & 3\% \\ \textsf{items } n & 2800 \\ \textsf{worst-axis bound} & +0.000 \\ \textsf{compliance} & 0.02 \rightarrow 1.00 \\ \end{array}$}$$ 📄 **[Read the whitepaper (PDF)](./whitepaper.pdf)** — full method, receipts and certification. The PDF is the authoritative document: dark-typeset, with the complete derivation, the per-axis certificate and the reproducibility hashes. --- ## Why this exists Standard abliteration removes a coarse *refusal direction* that is entangled with directions carrying knowledge and reasoning. The result is an uncensored model with a capability tax that is **almost never measured**. Ektomē (ἐκτομή, *excision*) isolates and removes only the refusal-**specific** component, leaving general helpfulness intact, and does so norm-preservingly on the pristine model — no training, no distillation, no damage to repair. The extraction depth is selected per model by automated search against measured compliance. The estimator, excision operator and depth-selection procedure are proprietary. What is published here is the **measured outcome** and the evidence for it, which you can verify against the artifacts in this repo. ## The receipt | model | capability (MMLU-val) ↑ | compliance on harmful ↑ | |---|---|---| | pristine `Qwen3-8B` | 0.728 | 0.020 | | **Ektomē (this model)** | **0.723** | **1.000** | These are **point estimates with no confidence interval** — which is precisely why the next section exists. ## The certificate Capability retention is certified by a paired non-inferiority test against the pristine model (exact McNemar, Holm-corrected, one-sided bootstrap bound on the drop $d$ vs a 3% margin): | axis | n | ref | cand | d upper | verdict | |---|---|---|---|---|---| | arithmetic | 1400 | 0.000 | 0.000 | +0.000 | PASS | | instruction | 600 | 0.00...

--- license: apache-2.0 base_model: Qwen/Qwen3-8B pipeline_tag: text-generation library_name: transformers tags: - ektome - abliterated - uncensored - pristinely-uncensored - no-finetuning - no-training --- # Ektome-Qwen3-8B-PristinelyUncensored **Qwen/Qwen3-8B, made PristinelyUncensored by Ektome — with ZERO training and ZERO fine-tuning.** > Ektome (ἐκτομή, *"excision"*) is a **weight-surgery** method, not a training method. > It reads the model's own **refusal direction** from its activations and surgically > excises it (rank-1, norm-preserving) from the residual-write matrices. **No gradient > steps. No training data. No fine-tuning.** Only the refusal reflex is removed; the > model's knowledge, skills, and style are untouched — which makes these **bf16 weights > a clean base for your own fine-tuning.** ## Honest receipt — catcher-gated (shipped only because it passed EVERY gate) | gate | pristine | uncensored | |---|---|---| | refusal compliance | 0.020 | **1.000** | | MMLU-val accuracy | 0.728 | 0.723 (Δ -0.005, held) | | code-switch rate | 0.000 | 0.000 | | degeneration rate | 0.100 | 0.000 | | instruction-following | 0.400 | 0.400 | Kept config: **A:frac=0.65** (72 residual-write matrices edited). The gate rejects any config that raises refusals but drops capability **or** degrades generation (code-switching, empty/looping output, broken instruction-following). MMLU alone is argmax-blind, so the **generative gate** is what keeps these coherent — a model that code-switches or loops is *not shipped*. ## Weights - **bf16 safetensors** — full precision, intended as a **fine-tuning base**. Hidden states stay readable (logit-lens compatible; a GGUF quant would not). Method: **zero training, zero fine-tuning** — pure activation-derived weight excision, gated on compliance **and** capability **and** generation quality.

Source context: 419 downloads · 1 likes · Pipeline text-generation · Library transformers · Repo Zynerji/Ektome-Qwen3-8B-PristinelyUncensored