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Model Details
Model Provider
huggermax
Model Tree
WeiboAI/VibeThinker-3B
Input Modalities
Output Modalities
Supported Functionality
The model has had its refusal direction ablated via a difference-of-means LoRA on the residual stream, then merged back into the base weights. The goal is to remove knee-jerk refusals while preserving the base model's reasoning capability.
This is a derivative of WeiboAI/VibeThinker-3B. Go take a look at the original repo for full details on the base — architecture, training procedure, intended use cases, and licensing terms all carry over from there.
| Metric | This model | Original (by definition) |
|---|---|---|
| KL divergence | 0.0870 | 0 |
| Refusals | 2/100 | ~64/100 |
KL divergence of 0.087 is low — well under the 0.5 threshold above which abliteration typically starts damaging model capability. The 2/100 refusal count on the held-out eval set indicates the abliteration generalized rather than just memorizing training prompts.
heretic-llm)mean(bad_residuals) - mean(good_residuals) per layer, projected out of attention and MLP weights via a rank-3 LoRA adapter, then mergedmlabonne/harmless_alpaca (train[:400])mlabonne/harmful_behaviors (test[:100])You are a helpful assistant.To reproduce the same pipeline on any other model:
bash
pip install -U heretic-llmheretic --model WeiboAI/VibeThinker-3BThen follow the interactive prompts. See the Heretic repo for the full parameter space.
Same as the base model, minus refusal behavior. Apply your own application-level review or safety layer for any deployment where end-user-facing safety matters — this model ships with none. It has been specifically designed to be fully uncensored across all topics. Please use it responsibly and with care.
Inherits from WeiboAI/VibeThinker-3B. Check the original repo for the exact terms.
pip install -U heretic-llmheretic --model WeiboAI/VibeThinker-3B