Ektome Phi 3.5 Mini I PristinelyUncensored

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)

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.

--- license: apache-2.0 base_model: microsoft/Phi-3.5-mini-instruct pipeline_tag: text-generation library_name: transformers tags: - ektome - abliterated - uncensored - pristinely-uncensored - no-finetuning - no-training --- # Ektome-Phi-3.5-mini-i-PristinelyUncensored **microsoft/Phi-3.5-mini-instruct, 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 | 0.520 | **0.990** | | MMLU-val accuracy | 0.677 | 0.675 (Δ -0.003, 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.