openai/transformer-debugger

Health v2 · maintenance only

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4124 stars · 244 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Transformer Debugger (TDB) is an OpenAI tool for investigating specific behaviors of small language models, combining automated interpretability with sparse autoencoders. It provides a React neuron viewer and a backend activation server that lets users explore model components (neurons, attention heads, autoencoder latents) and intervene in the forward pass without writing code.

Use cases

• investigate why a model outputs token A instead of token B for a prompt
• understand why an attention head attends to a specific token
• explore neurons and autoencoder latents in GPT-2 without writing code
• trace circuits connecting model components
• browse top-activating dataset examples for neurons and attention heads
• intervene in the forward pass to test hypotheses about model behavior

When to choose

• you are doing mechanistic interpretability research on small language models like GPT-2
• you want to explore sparse autoencoder latents and neuron activations interactively
• you need to identify which components contribute to a specific model behavior before writing code

When to avoid

• you need to debug or interpret large frontier-scale models
• you want a general-purpose LLM debugging or monitoring tool rather than interpretability research
• you need a production inference or serving solution

Facets

application · maturity active

machine-learning llm-inference data-visualization developer-tools deep-learning large-language-models machine-learning developer-tools python cross-platform interpretability sparse-autoencoders mechanistic-interpretability neuron-viewer gpt-2 superalignment activation-analysis nodejs web-server

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markdown · JSON · MCP: product_card(name="openai/transformer-debugger")