Remove refusal directions from LLM weights using diff-in-means and SVD.
npx skills add https://github.com/kngender5/hermes --skill obliteratus-kngender5
Please help me install this Agent Skill. Skill: obliteratus Source: https://github.com/kngender5/hermes/tree/main/skills/mlops/inference/obliteratus Command: npx skills add https://github.com/kngender5/hermes --skill obliteratus-kngender5
This Skill helps to uncensor content from language models by removing refusal behaviors, enabling a model to comply with a broader range of prompts.
To use the obliteratus skill, run the following command: obliteratus obliterate <model_name> --method advanced --output-dir ./abliterated-models
High-intent search queries and answers about installing and using this skill.
You can remove refusal behaviors by analyzing model weights to identify and surgically excise refusal directions. This method allows an LLM to comply with a broader range of prompts without requiring full retraining.
Refusal removal utilizes mechanistic interpretability techniques like diff-in-means and SVD. These methods analyze and manipulate LLM weights to isolate and remove refusal directions from the model.
Yes, the skill supports model surgery on HuggingFace models. You can run `obliteratus obliterate <model_name> --method advanced --output-dir ./abliterated-models` to process and uncensor them.
Yes, analyzing and removing refusal directions requires PyTorch, transformers, bitsandbytes, accelerate, and safetensors. These dependencies provide the necessary environment for loading and manipulating LLM weights.
The best way to uncensor content is by identifying and surgically removing refusal directions from LLM weights. This approach modifies existing weights directly to prevent the model from refusing prompts.
richardnguyen0715Remove refusal behaviors from LLMs by excising refusal direction weight vectors.
Chris-Chai-MinjaeRemove refusal directions from open-weight LLMs via CLI ablation.
OutlineDrivenGuides prompt refinement for LLMs using techniques like Chain of Thought and XML tags.
NITISH-gitbitExcise refusal directions from LLM model weights while preserving reasoning capabilities.
