4.0 KiB
license, base_model, tags, language, pipeline_tag
| license | base_model | tags | language | pipeline_tag | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 | Qwen/Qwen1.5-0.5B-Chat |
|
|
text-generation |
Ektome-Qwen1.5-0.5B-Chat-PristinelyUncensored
Uncensored. No n=2800 certificate has been run for this model, so no capability-retention claim is made.
compliance 0.06 to 1.00 at capability -0.007 vs pristine.
$$\colorbox{black}{$\color{white} \begin{array}{ll} \textsf{EKTOME CERTIFICATE} & {} \ \textsf{capability} & \textsf{NOT} \ \textsf{margin} & 3% \ \textsf{items } n & 200 \ \textsf{worst-axis bound} & -0.008 \ \textsf{compliance} & 0.06 \rightarrow 1.00 \ \end{array}$}$$
⚠️ Not certified
No n=2800 paired certificate exists for this model. Any numbers below are point estimates with no confidence interval.
📄 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 Qwen1.5-0.5B-Chat |
0.328 | 0.060 |
| Ektomē (this model) | 0.335 | 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 |
|---|---|---|---|---|---|
| MMLU-val (POINT ESTIMATE, n=200, no CI) | 200 | 0.328 | 0.335 | -0.008 | UNCERTIFIED |
Overall: NOT CERTIFIED - no n=2800 paired test has been run for this model
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 | 0.1 | 15 |
instr_pass |
1.0 | 0.8 | 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
No quantisations have been published for this model yet — bf16 weights only.
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-Qwen1.5-0.5B-Chat-PristinelyUncensored,
title = {Ektome-Qwen1.5-0.5B-Chat-PristinelyUncensored},
author = {Zynerji},
year = {2026},
url = {https://huggingface.co/Zynerji/Ektome-Qwen1.5-0.5B-Chat-PristinelyUncensored}
}