Interpretability Toolkit
Understanding transformer internals
The Interpretability Toolkit is an open-source library for understanding
what happens inside transformer-based language models. It provides
state-of-the-art probing techniques, visualization tools, and analysis
frameworks used by researchers worldwide.
The goal: make interpretability research accessible to anyone working
with large language models.
Probing a Model's Internal Representations
from interp_toolkit import Prober, AttentionVisualizer
from transformers import AutoModel
# Load any transformer model
model = AutoModel.from_pretrained("gpt2-large")
prober = Prober(model)
# Analyze what the model knows about syntax
results = prober.probe_for(
task="dependency_parsing",
layer_range=(8, 12),
examples=syntax_dataset
)
# Visualize attention patterns
viz = AttentionVisualizer(model)
viz.plot_attention_flow("The cat sat on the mat")
1.8kGitHub Stars
25kMonthly Downloads
150+Research Papers Using
32Contributors
Impact on Research
The toolkit has been cited in 150+ papers and is used by research teams at:
- Anthropic (alignment research)
- Google DeepMind (mechanistic interpretability)
- EleutherAI (open-source LLM research)
- Stanford HAI (human-AI interaction)
Several key findings from papers using this toolkit:
- Attention heads specialize for specific syntactic relations
- Model depth correlates with semantic abstraction
- Early layers encode surface features, late layers encode meaning