nnsight-remote-interpretability

You can run interpretability experiments on massive LLMs without local GPUs by using NDIF remote execution. This allows you to analyze models like 70B parameter architectures over the network instead of relying on local hardware.

Yes, you can analyze model internals for any PyTorch architecture. The tool provides universal support for Transformers, Mamba, and custom models, allowing you to interpret and manipulate neural networks regardless of the specific framework design.

You can perform multi-token generation interventions by leveraging the nnsight library to manipulate neural network internals. This enables gradient-based analysis and activation sharing during the generation process to understand how specific concepts are represented.

Yes, it supports deferred execution, allowing you to write code once that runs locally on small models or remotely on massive ones. This ensures your interpretability scripts remain portable across different model scales and execution environments.

The best way to analyze Llama-3.1-70B activations remotely is by using NDIF integration to execute gradient-based analysis and intervention scripts. This bypasses local resource limits and facilitates deep interpretability experiments on large-scale models.