Duplicate from rpeel/glitext-class-edge

Co-authored-by: RPeel <rpeel@users.noreply.huggingface.co>

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LICENSE

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README.md

@@ -0,0 +1,53 @@
+---
+library_name: glitext
+license: apache-2.0
+tags:
+- glitext
+glitext:
+  name: class-edge
+  label: GliText Classification (Fast)
+  description: An efficient zero-shot text classification model tuned for high throughput (speed).
+  recognition: false
+  classification: true
+  association: false
+  span_mode: false
+  size_gb: 0.17
+  hf_repo: rpeel/glitext-class-edge
+  source_url: knowledgator/gliclass-edge-v3.0
+---
+
+# rpeel/glitext-class-edge
+
+An efficient zero-shot text classification model tuned for high throughput (speed).
+
+## Requirements
+
+To download this model to the SAS GLiText server:
+
+```
+POST /v1/models/download?name=class-edge
+```
+
+To download and load into memory in one step:
+
+```
+PUT /v1/models?name=class-edge
+```
+
+## Source Model
+
+Exported from [knowledgator/gliclass-edge-v3.0](https://huggingface.co/knowledgator/gliclass-edge-v3.0).
+See the [original model card](https://huggingface.co/knowledgator/gliclass-edge-v3.0) for full architecture and training details.
+
+## Security Scan
+
+Scanned with [modelaudit](https://github.com/promptfoo/modelaudit) v0.2.40 on 2026-04-26. 16/16 checks passed. [Full results](modelaudit.json).
+
+
+| File | Size | SHA-256 |
+|------|------|---------|
+| `model.onnx` | 131.1 MB | `5289497ae11bb612…` |
+
+## License
+
+[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). Derived from [knowledgator/gliclass-edge-v3.0](https://huggingface.co/knowledgator/gliclass-edge-v3.0) by [knowledgator](https://huggingface.co/knowledgator).

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+  "tool": "modelaudit",
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+  "scanned_at": "2026-04-26T23:37:34Z",
+  "files": {
+    "model.onnx": {
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+  },
+  "audit": {
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+    "issues": [
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+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
+        "details": {
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+        "type": "onnx_check"
+      },
+      {
+        "message": "Layer 'model.text_projector.linear_2.weight' has 2 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
+        "timestamp": 1777246651.9025378,
+        "type": "onnx_check"
+      },
+      {
+        "message": "Layer 'onnx::MatMul_2099' has 2 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
+        "timestamp": 1777246651.9031963,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2119' has 1 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "timestamp": 1777246651.9038618,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2121' has 2 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+          "layer": "onnx::MatMul_2121",
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+        "why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
+        "timestamp": 1777246651.904512,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2122' has 8 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "timestamp": 1777246651.9051602,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2143' has 2 output neurons with abnormal weight magnitudes",
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+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
+        "timestamp": 1777246651.9058108,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2144' has 6 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "timestamp": 1777246651.9065673,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2163' has 1 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
+        "timestamp": 1777246651.9072561,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2165' has 2 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "timestamp": 1777246651.907795,
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+      {
+        "message": "Layer 'onnx::MatMul_2166' has 4 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "timestamp": 1777246651.908296,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2185' has 2 output neurons with abnormal weight magnitudes",
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+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "timestamp": 1777246651.908806,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2186' has 5 output neurons with abnormal weight magnitudes",
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+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "timestamp": 1777246651.909304,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2187' has 3 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "timestamp": 1777246651.9098086,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2188' has 3 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "timestamp": 1777246651.9103193,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2207' has 1 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
+        "timestamp": 1777246651.9108434,
+        "type": "onnx_check"
+      },
+      {
+        "message": "Layer 'onnx::MatMul_2208' has 5 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
+        "timestamp": 1777246651.9113624,
+        "type": "onnx_check"
+      },
+      {
+        "message": "Layer 'onnx::MatMul_2209' has 2 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
+        "timestamp": 1777246651.911881,
+        "type": "onnx_check"
+      },
+      {
+        "message": "Layer 'onnx::MatMul_2230' has 7 output neurons with abnormal weight magnitudes",
+        "severity": "info",
+        "location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
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+        "why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
+        "timestamp": 1777246651.9123945,
+        "type": "onnx_check"
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+      {
+        "message": "Layer 'onnx::MatMul_2231' has 1 output neurons with abnormal weight magnitudes",
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