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pyvene-interventions

Perform causal interventions on PyTorch models with declarative activation patching.

3|Updated Feb 21, 2026
Search Tags:#pytorch#causal inference#neural networks#interpretability#mechanistic interpretability#activation patching#causal tracing
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill pyvene-interventions-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pyvene-interventions
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/04-mechanistic-interpretability/pyvene
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill pyvene-interventions-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a framework for understanding and manipulating the internal workings of PyTorch models by performing causal interventions, enabling deeper insights into model behavior.

Core Features & Use Cases

  • Causal Tracing: Identify specific model components responsible for factual recall.
  • Activation Patching: Test hypotheses about the necessity of specific activations for model outputs.
  • Interchange Intervention Training (IIT): Train interventions to discover causal structures within models.
  • Model Steering: Modify model generation behavior at inference time.
  • Use Case: Debugging a language model that hallucinates by pinpointing and intervening on the specific neurons or layers responsible for generating incorrect information.

Quick Start

Use the pyvene-interventions skill to perform activation patching on a GPT-2 model by swapping activations between two different input prompts.

Frequently Asked Questions about pyvene-interventions

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform activation patching on a PyTorch model?▼

Activation patching on a PyTorch model swaps activations between different input prompts to test specific activations' necessity. This Skill uses a declarative framework to facilitate patching across any PyTorch architecture, including transformers.

Can I use causal interventions for model steering on state-space models?▼

Causal interventions support model steering to modify generation behavior at inference time. The framework works with any PyTorch architecture, including state-space models, vision models, and transformers.

What is interchange intervention training for neural networks?▼

Interchange intervention training (IIT) trains interventions to discover causal structures within neural networks. It provides a declarative framework to manipulate internal workings and gain deeper insights into model behavior.

Does this framework support zero interventions and addition interventions?▼

The framework supports various intervention types, including Vanilla, Addition, and Zero interventions. These options allow you to perform causal interventions flexibly across different PyTorch architectures.

How do I debug a language model that hallucinates using causal interventions?▼

Debugging a hallucinating language model involves pinpointing and intervening on specific neurons responsible for incorrect information. Causal interventions allow you to manipulate internal model components to identify and correct these errors.

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