Sparse Autoencoder Training & Analysis
技能 已验证 活跃Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
To enable researchers and practitioners to discover interpretable features within neural networks by training and analyzing Sparse Autoencoders.
功能
- Train custom Sparse Autoencoders
- Load and analyze pre-trained SAEs
- Decompose neural network activations into sparse features
- Perform feature attribution and steering
- Analyze superposition and monosemanticity
使用场景
- Discovering interpretable concepts learned by neural networks
- Analyzing feature interactions and superposition effects
- Studying safety-relevant features like bias or deception
- Performing feature-based model steering or ablation experiments
非目标
- Directly modifying neural network architectures beyond SAE integration
- Performing causal intervention experiments without SAE features
- Production deployment of steering mechanisms (focus is on analysis)
工作流
- Load model and pre-trained SAE
- Get model activations
- Encode activations to SAE features
- Analyze features and reconstruction
- Optionally, train a custom SAE
- Analyze feature attribution and steering
实践
- Mechanistic Interpretability
- Feature Engineering
- Model Analysis
先决条件
- Python 3.10+
- transformer-lens>=2.0.0
- torch>=2.0.0
- sae-lens>=6.0.0
安装
请先添加 Marketplace
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skills质量评分
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