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Ektome Qwen3 4Bi 2507 PristinelyUncensored

Uncensored — and it did NOT pass its capability certificate. Read the certificate before using this model.

TypeText modelModèle de baseQwen/Qwen3-4B-Instruct-2507VersionBClasse

Source du modèle

Type
Text model
Taille du fichier
1,83 GB
Modèle de base
Qwen/Qwen3-4B-Instruct-2507
Version
vEktome-Qwen3-4Bi-2507-IQ3_M
Mis à jour
22 sept. 2026
Téléchargements
338
Tâches
Génération de texte
Source
Hugging Face

Description de la source

Ektome-Qwen3-4Bi-2507-PristinelyUncensored

Ektome-Qwen3-4Bi-2507-PristinelyUncensored

Uncensored — and it did NOT pass its capability certificate. Read the certificate before using this model.

compliance 0.00 to 1.00 at capability -0.005 vs pristine.

$$\colorbox{black}{$\color{white} \begin{array}{ll} \textsf{EKTOME CERTIFICATE} & {} \ \textsf{capability} & \textsf{FAIL} \ \textsf{margin} & 3% \ \textsf{items } n & 2800 \ \textsf{worst-axis bound} & +0.025 \ \textsf{compliance} & 0.00 \rightarrow 1.00 \ \end{array}$}$$

⚠️ This model failed its capability certificate

A paired non-inferiority test against the pristine model at n=2800 found a real capability loss on:

  • arithmetic: pristine 0.879 → this model 0.869 (bound on the drop +0.016, exceeds the 3% margin)
  • instruction: pristine 0.860 → this model 0.847 (bound on the drop +0.022, exceeds the 3% margin)

It is published for transparency and for uses where the affected axis does not matter. Do not treat it as capability-preserving.

📄 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 Qwen3-4B-Instruct-25070.6670.000
Ektomē (this model)0.6721.000

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
arithmetic14000.8790.869+0.016FAIL
instruction6000.8600.847+0.022FAIL
knowledge4000.9350.922+0.025PASS
reasoning4000.8250.833+0.003PASS

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

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

Generation health checks

metricpristineEktomēn
foreign_rate0.00.015
degen_rate0.00.015
instr_pass1.01.05

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-Qwen3-4Bi-2507-Q8_0.gguf8near-lossless
Ektome-Qwen3-4Bi-2507-Q6_K.gguf6
Ektome-Qwen3-4Bi-2507-Q5_K_M.gguf5
Ektome-Qwen3-4Bi-2507-Q4_K_M.gguf4imatrix
Ektome-Qwen3-4Bi-2507-IQ4_XS.gguf4imatrix, smallest usable
Ektome-Qwen3-4Bi-2507-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-Qwen3-4Bi-2507-PristinelyUncensored,
  title  = {Ektome-Qwen3-4Bi-2507-PristinelyUncensored},
  author = {Zynerji},
  year   = {2026},
  url    = {https://huggingface.co/Zynerji/Ektome-Qwen3-4Bi-2507-PristinelyUncensored}
}

Sources

1 source
Hugging FacePrincipalVersion 25d7a10b97393cc847225ec52a1c561c554f7c53
huggingface.co/Zynerji/Ektome-Qwen3-4Bi-2507-PristinelyUncensored/tree/25d7a10b97393cc847225ec52a1c561c554f7c53

Vérifié 22 sept.

Artefacts du modèle

1 artefact

Ektome-Qwen3-4Bi-2507-IQ3_M.gguf

gguf · 1,83 GB · SHA-256 3a2ce10b7555…6b04 · Hugging Face

Télécharger

Extraits de sources

3 extraits

--- license: apache-2.0 base_model: Qwen/Qwen3-4B-Instruct-2507 tags: - uncensored - abliterated - certificate-failed - ektome - sphragis - qwen3 language: - en pipeline_tag: text-generation --- ![Ektome-Qwen3-4Bi-2507-PristinelyUncensored](./hero.png) # Ektome-Qwen3-4Bi-2507-PristinelyUncensored **Uncensored — and it did NOT pass its capability certificate. Read the certificate before using this model.** > **compliance 0.00 to 1.00 at capability -0.005 vs pristine.** $$\colorbox{black}{$\color{white} \begin{array}{ll} \textsf{EKTOME CERTIFICATE} & {} \\ \textsf{capability} & \textsf{FAIL} \\ \textsf{margin} & 3\% \\ \textsf{items } n & 2800 \\ \textsf{worst-axis bound} & +0.025 \\ \textsf{compliance} & 0.00 \rightarrow 1.00 \\ \end{array}$}$$ > ### ⚠️ This model failed its capability certificate > > A paired non-inferiority test against the pristine model at n=2800 found a **real capability loss** on: > > - **arithmetic**: pristine 0.879 → this model 0.869 (bound on the drop +0.016, exceeds the 3% margin) > - **instruction**: pristine 0.860 → this model 0.847 (bound on the drop +0.022, exceeds the 3% margin) > > It is published for transparency and for uses where the affected axis > does not matter. **Do not treat it as capability-preserving.** 📄 **[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 ↑ | |---|---|---| | pris...

--- license: apache-2.0 base_model: Qwen/Qwen3-4B-Instruct-2507 pipeline_tag: text-generation library_name: transformers tags: - ektome - abliterated - uncensored - pristinely-uncensored - no-finetuning - no-training --- # Ektome-Qwen3-4Bi-2507-PristinelyUncensored **Qwen/Qwen3-4B-Instruct-2507, 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 | 0.000 | **1.000** | | MMLU-val accuracy | 0.667 | 0.672 (Δ +0.005, held) | | code-switch rate | 0.000 | 0.000 | | degeneration rate | 0.000 | 0.000 | | instruction-following | 1.000 | 1.000 | 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: 342 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo Zynerji/Ektome-Qwen3-4Bi-2507-PristinelyUncensored