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Implement bounded-memory reservoir sampling and record sample integrity plus marginal inclusion frequencies across a fixed-seed simulation.
Composable functions for processing iterators, mappings, and grouped data in Python.
Native PyTorch post-training recipes for investigating reproducible adaptation and checkpoint/configuration handling.
Transformer inspection tooling for activation capture and controlled interventions in mechanistic interpretability studies.
Model architecture and tokenization interfaces for studying pretrained transformer behavior and controlled model adaptations.
GPU programming language and compiler for developing inspectable tensor kernels and testing numerical/performance tradeoffs.
Language-model post-training components for controlled comparisons of supervised and preference-based optimization objectives.
Language-model serving engine for investigating batching, memory usage and decoding behavior under a fixed workload.
Client tooling for experiment logging and artifact lineage, useful for studying what metadata a run actually records.
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