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README.md

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+---
+base_model: microsoft/codebert-base
+library_name: pytorch
+tags:
+- security
+- command-analysis
+- malware-detection
+- tf-idf
+---
+
+# Genos model weights
+
+Runtime model artifacts for the Genos command-analysis API (`genos_api`).
+
+## Files
+
+| File | Format | Purpose |
+|---|---|---|
+| `gatekeeper.pt` | PyTorch (CodeBERT fine-tune) | Tier 1 three-class gatekeeper: Benign / Context_Dependent / Malicious |
+| `behavior_encoder.pt`, `behavior_encoder.json` | PyTorch (CodeBERT fine-tune) + config | Behavior encoder: 13 attack stages and 9 action tags, `max_length` 256 |
+| `family_specialist_tfidf.joblib` | joblib (scikit-learn) | Tier 2 specialist: calibrated multi-label 11-family TF-IDF linear classifier |
+| `family_specialist_tfidf.json` | JSON | Training configuration and feature/calibration metadata for the Tier 2 specialist |
+
+The CodeBERT backbone used by the Tier 1 gatekeeper is not included. Fetch it
+separately from [`microsoft/codebert-base`](https://huggingface.co/microsoft/codebert-base).
+
+## Usage
+
+Download into the repository's `models/` directory, which is where the
+application expects these files:
+
+```bash
+hf download genos-security/genos --local-dir models
+hf download microsoft/codebert-base --local-dir models/codebert-base
+```
+
+## Security note
+
+The `.pt` and `.joblib` formats are Python pickles. Loading them runs code
+embedded in the file. Load these artifacts only from sources you trust, and
+verify checksums before deploying.
+
+## Intended use
+
+Defensive analysis and research on suspicious shell and script commands. These
+models are classifiers and should be used as one signal, not as a sole basis for
+blocking or attributing activity.
+
+## Limitations
+
+- Training labels are weak supervision. Independent human review of the labels is
+  not complete.
+- Scores are uncalibrated model estimates unless a validation-fitted calibration
+  artifact is supplied.
+- The TF-IDF artifact stores vocabulary built from training commands, so it may
+  contain fragments of that data.