Results for “mechanistic-interpretability”
8 skills
orchestra-research Sparse Autoencoder Training
Train and analyze Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features for mechanistic interpretability research.
10.4k · bundle
orchestra-research Nnsight Remote Interpretability
Run interpretability experiments on neural network internals using nnsight, with optional NDIF remote execution for massive models.
10.4k · bundle
More results
qhjqhj00 Shap
Explains machine learning model predictions using SHAP values, covering feature importance, visualization plots, model debugging, bias analysis, and production deployment.
3 · bundle
lingxling Shap
Explains machine learning model predictions using SHAP values, covering feature importance, visualizations, debugging, bias analysis, and production deployment.
253 · bundle
orchestra-research Speculative Decoding
Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques for 1.5-3.6× speedup without quality loss.
10.4k · bundle
paramchordiya Ml Engineering
Enforces rigorous ML modeling, feature engineering, training, and evaluation standards at principal-engineer level.
0
orchestra-research Pyvene Interventions
Perform causal interventions on PyTorch models using pyvene's declarative framework for causal tracing, activation patching, and interchange intervention training.
10.4k · bundle
lord1egypt Sparse Autoencoder Training
Trains and analyzes Sparse Autoencoders (SAEs) with SAELens to decompose neural network activations into interpretable features, covering loading pre-trained SAEs, training custom ones, and feature steering.
2