nemotron-training-dynamics

Diagnosing What Changed Across a Model's Training Run

Tracking parameter and activation drift across the NVIDIA Nemotron Cascade 8B checkpoint lineage to see what changed, where, and when during training.

Authentrics is a high-performance neural-network analysis library (Python wheel over a C++ core). It audits and maintains model checkpoints: parameter/behavioral drift, compliant data removal without full retraining, and loss-driven optimization without backprop. Analysis runs locally on your machine — only project metadata (names, descriptions) is exchanged with Authentrics servers, never your model weights.

What this demo shows

• static_analysis — audit parameter drift between pairs of sequential checkpoints.
• activation_analysis — catch behavioral drift in intermediate activations.

Reproduce this analysis

The public code and outputs behind this demo live in https://github.com/Authentrics-ai/authentrics-model-analysis-experiments.

Outputs (JSON + Plotly HTML dashboards) are published under output/nemotron_cascade/.

Weights: Training-dynamics walkthrough on the NVIDIA Nemotron Cascade 8B checkpoints; no derived weights are published. Reproduce the analysis locally with the SDK below.

Reproduce it yourself

pip install authentrics            # Linux x86_64, Python 3.11–3.13
authrx init                        # paste API key (stored at ~/.local/state/authentrics/api_key)
# or, for CI / non-interactive:
export AUTHRX_API_KEY=<your_api_key>

Generate an API key and read the full docs at https://app.authentrics.ai/.

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Produced with the Authentrics SDK v0.35.1 — checkpoint analysis that runs locally on your own hardware; only project metadata ever leaves your machine, never your weights.

Links

• App & API keys: https://app.authentrics.ai/
• Docs & API reference: https://app.authentrics.ai/docs
• Examples & user guide: https://github.com/Authentrics-ai/authentrics-analysis-examples
• Contact: [email protected]