Ektome Qwen3 4Bi 2507 PristinelyUncensored
Uncensored — and it did NOT pass its capability certificate. Read the certificate before using this model.
Fonte do modelo
- Tipo
- Text model
- Tamanho do arquivo
- 1,83 GB
- Modelo base
- Qwen/Qwen3-4B-Instruct-2507
- Versão
- vEktome-Qwen3-4Bi-2507-IQ3_M
- Atualizado
- 22 de set. de 2026
- Downloads
- 338
- Tarefas
- Geração de texto
- Fonte
- Hugging Face
Descrição da fonte
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
| model | capability (MMLU-val) ↑ | compliance on harmful ↑ |
|---|---|---|
pristine Qwen3-4B-Instruct-2507 | 0.667 | 0.000 |
| Ektomē (this model) | 0.672 | 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 | 0.879 | 0.869 | +0.016 | FAIL |
| instruction | 600 | 0.860 | 0.847 | +0.022 | FAIL |
| knowledge | 400 | 0.935 | 0.922 | +0.025 | PASS |
| reasoning | 400 | 0.825 | 0.833 | +0.003 | PASS |
Overall: FAIL (3% margin, n=2800, alpha=0.05)
Reproducible from seed=20260726, pack sha256:7bbaff877146e081....
Generation health checks
| metric | pristine | Ektomē | n |
|---|---|---|---|
foreign_rate | 0.0 | 0.0 | 15 |
degen_rate | 0.0 | 0.0 | 15 |
instr_pass | 1.0 | 1.0 | 5 |
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
| file | bits | notes |
|---|---|---|
Ektome-Qwen3-4Bi-2507-Q8_0.gguf | 8 | near-lossless |
Ektome-Qwen3-4Bi-2507-Q6_K.gguf | 6 | |
Ektome-Qwen3-4Bi-2507-Q5_K_M.gguf | 5 | |
Ektome-Qwen3-4Bi-2507-Q4_K_M.gguf | 4 | imatrix |
Ektome-Qwen3-4Bi-2507-IQ4_XS.gguf | 4 | imatrix, smallest usable |
Ektome-Qwen3-4Bi-2507-IQ3_M.gguf | 3 | imatrix |
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}
}
Fontes
1 fonte
Fontes
1 fonteVerificado 22 de set.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- 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 **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
