transformersjs-support

README.md

@@ -0,0 +1,3 @@
+---
+license: mit
+---

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "mshenoda/roberta-spam",
+  "_name_or_path": "/content/roberta_spam_onnx",
   "architectures": [
     "RobertaForSequenceClassification"
   ],

onnx/model.onnx → model.onnx

@@ -1,3 +1,3 @@
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+oid sha256:dc1caec77e221ec39fe677b7836d67cc3d1791da7b275da2957fe8e06fc507b8
+size 125560622

onnx/model_quantized.onnx → model_quantized.onnx

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+oid sha256:86293e47c297202d38d98dd78e172dfc2ac2b65456c35fa090c90119d45fe406
+size 125528143

onnx/model_bnb4.onnx

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-size 206977448

onnx/model_fp16.onnx

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onnx/model_int8.onnx

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-size 125871653

onnx/model_q4.onnx

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onnx/model_q4f16.onnx

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onnx/model_uint8.onnx

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ort_config.json

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+{
+  "one_external_file": true,
+  "opset": null,
+  "optimization": {},
+  "quantization": {
+    "activations_dtype": "QUInt8",
+    "activations_symmetric": false,
+    "format": "QOperator",
+    "is_static": false,
+    "mode": "IntegerOps",
+    "nodes_to_exclude": [],
+    "nodes_to_quantize": [],
+    "operators_to_quantize": [
+      "Conv",
+      "MatMul",
+      "Attention",
+      "LSTM",
+      "Gather",
+      "Transpose",
+      "EmbedLayerNormalization"
+    ],
+    "per_channel": false,
+    "qdq_add_pair_to_weight": false,
+    "qdq_dedicated_pair": false,
+    "qdq_op_type_per_channel_support_to_axis": {
+      "MatMul": 1
+    },
+    "reduce_range": false,
+    "weights_dtype": "QInt8",
+    "weights_symmetric": true
+  },
+  "use_external_data_format": false
+}

quantize_config.json

@@ -1,17 +0,0 @@
-{
-    "modes": [
-        "fp16",
-        "q8",
-        "int8",
-        "uint8",
-        "q4",
-        "q4f16",
-        "bnb4"
-    ],
-    "per_channel": true,
-    "reduce_range": true,
-    "block_size": null,
-    "is_symmetric": true,
-    "accuracy_level": null,
-    "quant_type": 1
-}