Can I use pyvene for model steering?

Yes, pyvene allows you to apply interventions during the generation process, which can be used to steer models toward specific behaviors, such as increased honesty or toxicity reduction.

What is Interchange Intervention Training (IIT)?

IIT is a technique supported by pyvene where you train a model or a specific intervention to align a neural representation with a structured causal model, helping discover interpretable subspaces.

What is the primary use of the pyvene skill?

The pyvene skill is used for mechanistic interpretability, specifically for performing causal interventions like activation patching and causal tracing to understand how neural networks make decisions.

Does pyvene only work with Transformer models?

No, pyvene is designed to work with any PyTorch model, although it includes many built-in helpers for popular Transformer architectures from HuggingFace.

pyvene Causal Interventions

Name: pyvene Causal Interventions
Author: zechenzhangAGI

byzechenzhangAGI

•

384

データサイエンスとML

Performs declarative causal interventions and mechanistic interpretability experiments on PyTorch models.

概要

pyvene is a specialized library designed for researchers and engineers to conduct rigorous causal intervention experiments on neural networks. By providing a declarative, dict-based framework for activation patching, causal tracing, and interchange intervention training (IIT), it allows users to locate factual associations, test causal hypotheses about model components, and train steerable models. This skill bridges the gap between theoretical interpretability research and practical PyTorch implementation, facilitating reproducible and shareable AI research directly within the Claude Code environment.

主な機能

Interchange Intervention Training (IIT) for causal structure discovery
384 GitHub stars
Built-in support for Distributed Alignment Search (DAS) and model steering
Support for varied intervention types including Vanilla, Addition, and Rotated Space
Seamless integration with HuggingFace models and any PyTorch architecture
Declarative framework for activation patching and causal tracing

ユースケース

Steering large language model behavior during inference using pre-trained interventions
Performing ROME-style causal tracing to locate stored knowledge in LLMs
Conducting circuit analysis through activation patching to identify necessary components

概要

主な機能

Interchange Intervention Training (IIT) for causal structure discovery
384 GitHub stars
Built-in support for Distributed Alignment Search (DAS) and model steering
Support for varied intervention types including Vanilla, Addition, and Rotated Space
Seamless integration with HuggingFace models and any PyTorch architecture
Declarative framework for activation patching and causal tracing

ユースケース

Steering large language model behavior during inference using pre-trained interventions
Performing ROME-style causal tracing to locate stored knowledge in LLMs
Conducting circuit analysis through activation patching to identify necessary components