Pyvene Interventions
技能 已验证 活跃Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
To enable researchers and developers to perform reproducible causal interventions on PyTorch models, test hypotheses about model behavior, and understand model components.
功能
- Declarative intervention framework
- Support for activation patching and causal tracing
- Interchange Intervention Training (IIT)
- Saving and sharing interventions via HuggingFace
- Compatibility with any PyTorch model
使用场景
- Testing causal hypotheses about model components
- Reproducing and sharing intervention experiments
- Conducting ROME-style causal tracing
- Performing activation patching for circuit analysis
非目标
- Exploratory activation analysis (use TransformerLens)
- Training/analyzing SAEs (use SAELens)
- Remote execution on massive models (use nnsight)
- Lower-level control than pyvene offers (use nnsight)
Trust
- info:Issues AttentionThere are 17 open issues and 4 closed issues in the last 90 days, indicating a closure rate below 50% and a moderate level of engagement.
安装
npx skills add davila7/claude-code-templates通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
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