Inseq

Inseq addresses a real pain point: understanding why a generative model output a specific token. The toolkit's integration with Hugging Face Transformers and Captum makes it practical, and the code samples on the site work. We'd pick Inseq when doing research on model interpretability for NLG or debugging a translation or text generation model. Where it shines is its focus on sequence generation—most attribution tools are built for classifiers. Inseq supports encoder-decoder and decoder-only models, and includes methods like Integrated Gradients that are well-studied. The Tuned Lens for confidence estimation and Petals integration for distributed LLMs are nice extras. However, Inseq is not for everyone. If you need a GUI or don't code, look elsewhere. It's also early-stage—some documentation is sparse, and the API may change. The small team means slower issue resolution. For production teams needing SLAs, this isn't it. Compared to Captum alone, Inseq provides a higher-level API and model-specific focus, so you write less code. But if you already use Captum, you might not need the abstraction. Similarly, tools like LIT (Language Interpretability Tool) offer visual exploration without coding, but lack Inseq's deep attribution methods. In practice, expect some friction: not all Transformers models are supported, and attribution can be slow for long sequences. The community is active on Discord, which helps. For researchers and advanced practitioners, Inseq is worth the learning curve.