MI Analysis Operational Reference
CommunityEnd-to-end MI analysis for prompt interpretability.
Data & Analytics#huggingface#mi-analysis#prompt-interpretability#attention-capture#logit-lens#region-annotation#gpu-analysis
Authortaylorsatula
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This operational guide documents how to run TeaLeaves' mechanistic interpretability (MI) pipeline end-to-end, covering region annotation, attention capture, logit-lens projections, and result rendering to support prompt engineering decisions.
Core Features & Use Cases
- Region annotation: define named spans in prompts and map them to token positions to analyze focus and region interactions.
- Attention capture & logit lens: hook attention across layers and project residual streams to understand token decisions.
- Visualization & comparison: generate heatmaps, cooking curves, layer sweeps, and multi-sample reports to compare variants and seeds.
Quick Start
Run the MI analysis workflow on a test_cases.json using the GPU-enabled run_analysis.py to produce per-case results and visuals.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: MI Analysis Operational Reference Download link: https://github.com/taylorsatula/TeaLeaves/archive/main.zip#mi-analysis-operational-reference Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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