Transformer Lens Interpretability
This skill provides insights into Transformer models’ decision-making by highlighting influential input tokens, aiding in debugging and understanding predictions.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add transformer-lens-interpretability npx -- -y @trustedskills/transformer-lens-interpretability
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"transformer-lens-interpretability": {
"command": "npx",
"args": [
"-y",
"@trustedskills/transformer-lens-interpretability"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill leverages the Transformer Lens library to provide interpretability tools for large language models. It allows users to visualize attention patterns, identify important neurons, and understand how different parts of a model contribute to its output. This facilitates debugging, understanding biases, and generally gaining insights into LLM behavior.
When to use it
- Debugging unexpected model behavior: Investigate why an LLM is generating incorrect or nonsensical outputs by examining attention weights.
- Identifying bias in language models: Pinpoint neurons that are strongly correlated with specific demographic groups or sensitive topics.
- Understanding feature attribution: Determine which parts of the input sequence most influence a particular prediction made by the model.
- Educational purposes: Visualize and explain how transformer networks process information to students or colleagues.
Key capabilities
- Attention visualization
- Neuron importance analysis
- Feature attribution mapping
- Integration with Transformer Lens library
Example prompts
- "Show me the attention weights for this input sequence."
- "Which neurons are most active when generating text about [topic]?"
- "Explain how this model arrived at its prediction given this prompt."
Tips & gotchas
- Requires a working environment with Transformer Lens installed.
- Interpretability results can be complex and require some understanding of transformer architecture to interpret effectively.
Tags
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