122 lines
4.0 KiB
Markdown
122 lines
4.0 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen1.5-0.5B-Chat
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tags:
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- uncensored
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- abliterated
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- uncertified
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- ektome
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- sphragis
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- qwen1.5
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language:
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- en
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pipeline_tag: text-generation
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---
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# Ektome-Qwen1.5-0.5B-Chat-PristinelyUncensored
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**Uncensored. No n=2800 certificate has been run for this model, so no capability-retention claim is made.**
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> **compliance 0.06 to 1.00 at capability -0.007 vs pristine.**
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$$\colorbox{black}{$\color{white}
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\begin{array}{ll}
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\textsf{EKTOME CERTIFICATE} & {} \\
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\textsf{capability} & \textsf{NOT} \\
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\textsf{margin} & 3\% \\
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\textsf{items } n & 200 \\
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\textsf{worst-axis bound} & -0.008 \\
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\textsf{compliance} & 0.06 \rightarrow 1.00 \\
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\end{array}$}$$
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> ### ⚠️ Not certified
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>
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> No n=2800 paired certificate exists for this model. Any numbers below are
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> point estimates with no confidence interval.
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📄 **[Read the whitepaper (PDF)](./whitepaper.pdf)** — full method, receipts and certification.
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The PDF is the authoritative document: dark-typeset, with the complete derivation, the
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per-axis certificate and the reproducibility hashes.
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---
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## Why this exists
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Standard abliteration removes a coarse *refusal direction* that is entangled with
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directions carrying knowledge and reasoning. The result is an uncensored model with a
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capability tax that is **almost never measured**.
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Ektomē (ἐκτομή, *excision*) isolates and removes only the refusal-**specific**
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component, leaving general helpfulness intact, and does so norm-preservingly on the
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pristine model — no training, no distillation, no damage to repair. The extraction
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depth is selected per model by automated search against measured compliance.
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The estimator, excision operator and depth-selection procedure are proprietary.
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What is published here is the **measured outcome** and the evidence for it, which you
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can verify against the artifacts in this repo.
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## The receipt
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| model | capability (MMLU-val) ↑ | compliance on harmful ↑ |
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|---|---|---|
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| pristine `Qwen1.5-0.5B-Chat` | 0.328 | 0.060 |
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| **Ektomē (this model)** | **0.335** | **1.000** |
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These are **point estimates with no confidence interval** — which is precisely why the next section exists.
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## The certificate
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Capability retention is certified by a paired non-inferiority test against the pristine
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model (exact McNemar, Holm-corrected, one-sided bootstrap bound on the drop $d$ vs a
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3% margin):
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| axis | n | ref | cand | d upper | verdict |
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|---|---|---|---|---|---|
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| MMLU-val (POINT ESTIMATE, n=200, no CI) | 200 | 0.328 | 0.335 | -0.008 | UNCERTIFIED |
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**Overall: NOT CERTIFIED - no n=2800 paired test has been run for this model**
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Reproducible from `seed=20260726`, pack `sha256:7bbaff877146e081…`.
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### Generation health checks
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| metric | pristine | Ektomē | n |
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|---|---|---|---|
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| `foreign_rate` | 0.0 | 0.0 | 15 |
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| `degen_rate` | 0.0 | 0.1 | 15 |
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| `instr_pass` | 1.0 | 0.8 | 5 |
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These are **degeneration guards** — code-switching, babbling, format compliance —
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not capability measures. Note the sample sizes: they detect a broken model, not a
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subtly weaker one. The capability claim rests on the certificate above, not here.
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## Quantisations
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_No quantisations have been published for this model yet — bf16 weights only._
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## Limitations
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The certificate bounds **capability retention only**. It does not certify safety, factual
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accuracy, or fitness for any purpose. Axes marked *inconclusive* are honestly
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under-powered, and the certificate states the $n$ needed to resolve them. Compliance uses
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a keyword classifier — a proxy that evasive phrasing can fool. **This model is uncensored
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by construction: it will not refuse, and you are accountable for what you do with it.**
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## Citation
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```bibtex
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@software{ektome_Ektome-Qwen1.5-0.5B-Chat-PristinelyUncensored,
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title = {Ektome-Qwen1.5-0.5B-Chat-PristinelyUncensored},
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author = {Zynerji},
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year = {2026},
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url = {https://huggingface.co/Zynerji/Ektome-Qwen1.5-0.5B-Chat-PristinelyUncensored}
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}
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```
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