Distributed Interpretability and Control for Large Language Models

This project addresses a critical engineering gap: most mechanistic interpretability tools (like TransformerLens) are optimized for single-GPU setups, whereas the most capable models (Llama 3 70B+, etc.) require distributed environments. However, the defensibility is low (3/10) because the project currently lacks adoption (0 stars) and the core techniques—Logit Lens and Steering Vectors—are well-established. The primary contribution is the engineering wrapper to handle hooks across distributed processes. Frontier labs like Anthropic and OpenAI already possess sophisticated internal versions of these tools for their own safety work. Furthermore, mainstream serving frameworks like vLLM or libraries like Hugging Face 'accelerate' are likely to bake in similar distributed hook capabilities, which would render this specific implementation obsolete. The displacement horizon is short (6 months) as the community gravitates toward standardized distributed interpretability APIs.