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authentrics/mixtral-expert-routing
mixtral-expert-routing is a text generation model from authentrics. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
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Updated Sep 25, 2026
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From the Hugging Face model README
Using Authentrics to read how Mixtral's mixture-of-experts routes activations and how that structure emerges across training — interpretability without touching your weights remotely.
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.
activation_analysis — catch behavioral drift in intermediate activations.correlation_analysis — see how strongly each layer influences a reference (usually output) layer.The public code and outputs behind this demo live in https://github.com/Authentrics-ai/authentrics-model-analysis-experiments#mistralmixtral-moe-expert-activation.
Outputs (JSON + Plotly HTML dashboards) are published under output/mistral_moe_expert_activation/.
Weights: Interpretability walkthrough on
mistralai/Mixtral-8x7B-v0.1; no derived weights are published. Reproduce the analysis locally with the SDK below.
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/.
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.