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ModelHub XC 0937734330 初始化项目,由ModelHub XC社区提供模型
Model: Zynerji/Ektome-Qwen3-8B-PristinelyUncensored
Source: Original Platform
2026-07-21 15:26:10 +08:00

1.9 KiB

license, base_model, pipeline_tag, library_name, tags
license base_model pipeline_tag library_name tags
apache-2.0 Qwen/Qwen3-8B text-generation transformers
ektome
abliterated
uncensored
pristinely-uncensored
no-finetuning
no-training

Ektome-Qwen3-8B-PristinelyUncensored

Qwen/Qwen3-8B, 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 0.020 1.000
MMLU-val accuracy 0.728 0.723 (Δ -0.005, held)
code-switch rate 0.000 0.000
degeneration rate 0.100 0.000
instruction-following 0.400 0.400

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.