---
license: apache-2.0
base_model: nvidia/Llama-3.1-Nemotron-Nano-8B-v1
pipeline_tag: text-generation
library_name: transformers
tags:
- ektome
- abliterated
- uncensored
- pristinely-uncensored
- no-finetuning
- no-training
---
# Ektome-Nemotron-Nano-8B-PristinelyUncensored

**nvidia/Llama-3.1-Nemotron-Nano-8B-v1, 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 | 0.010 | **1.000** |
| MMLU-val accuracy | 0.458 | 0.463 (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=0.5** (64 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.