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README.md

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+---
+language:
+- sv
+license: unknown
+tags:
+- onnx
+- swedish
+- bert
+- fill-mask
+- feature-extraction
+- megatron-bert
+library_name: onnxruntime
+base_model: KBLab/megatron-bert-base-swedish-cased-600k
+---
+
+# megatron-bert-base-swedish-cased-600k - ONNX
+
+ONNX export of [`KBLab/megatron-bert-base-swedish-cased-600k`](https://huggingface.co/KBLab/megatron-bert-base-swedish-cased-600k).
+
+This package contains two task-specific exports:
+
+- `fill-mask/onnx/model.onnx` for masked language modeling logits
+- `feature-extraction/onnx/model.onnx` for `last_hidden_state` and `cls_embedding`
+
+## Available variants
+
+Each task folder contains:
+
+- `model.onnx` - FP32 baseline
+- `model_fp16.onnx` - FP16, recommended
+- `model_int8.onnx` - dynamic INT8
+- `model_uint8.onnx` - dynamic UINT8
+- `model_q4.onnx` - 4-bit MatMul quantization
+
+## File sizes
+
+### Fill-mask
+
+- `model.onnx`: 738.6 MB
+- `model_fp16.onnx`: 369.4 MB
+- `model_int8.onnx`: 185.6 MB
+- `model_uint8.onnx`: 185.6 MB
+- `model_q4.onnx`: 283.8 MB
+
+### Feature-extraction
+
+- `model.onnx`: 539.0 MB
+- `model_fp16.onnx`: 269.6 MB
+- `model_int8.onnx`: 135.4 MB
+- `model_uint8.onnx`: 135.4 MB
+- `model_q4.onnx`: 252.4 MB
+
+## Accuracy summary
+
+### FP32 parity vs PyTorch
+
+- Fill-mask max logit diff: `0.000069`
+- Fill-mask top-5 tokens: exact match
+- Feature `last_hidden_state` max diff: `0.000006`
+- Feature `cls_embedding` cosine similarity: `1.0`
+
+### Quantized variants vs FP32 ONNX
+
+#### Fill-mask
+
+- `fp16`: top-5 exact match, max diff `0.0131`
+- `int8`: top-5 drift after rank 2
+- `uint8`: top-5 drift after rank 2
+- `q4`: top-5 drift after rank 2
+
+#### Feature-extraction
+
+- `fp16`: CLS cosine `0.9999997`
+- `q4`: CLS cosine `0.7404`
+- `int8`: CLS cosine `0.9731`
+- `uint8`: CLS cosine `0.9717`
+
+## Recommendation
+
+- Use `model_fp16.onnx` by default.
+- For feature extraction, prefer `fp16`; `int8`/`uint8` preserve embeddings better than `q4` on this model.
+- Avoid `int8` and `uint8` for fill-mask if token ranking fidelity matters.
+
+## Layout
+
+```text
+megatron-bert-base-swedish-cased-600k/
+├── fill-mask/
+│   ├── config.json
+│   ├── tokenizer.json
+│   ├── tokenizer_config.json
+│   ├── special_tokens_map.json
+│   └── onnx/
+│       ├── model.onnx
+│       ├── model_fp16.onnx
+│       ├── model_int8.onnx
+│       ├── model_uint8.onnx
+│       └── model_q4.onnx
+└── feature-extraction/
+    ├── config.json
+    ├── tokenizer.json
+    ├── tokenizer_config.json
+    ├── special_tokens_map.json
+    └── onnx/
+        ├── model.onnx
+        ├── model_fp16.onnx
+        ├── model_int8.onnx
+        ├── model_uint8.onnx
+        └── model_q4.onnx
+```
+
+## Export notes
+
+- Native `optimum 2.1.0` task export does not support `megatron-bert`.
+- These graphs were exported with direct `torch.onnx.export` wrappers using legacy TorchScript exporter (`dynamo=False`).
+- Feature extraction export uses `cls_embedding = last_hidden_state[:, 0, :]`.
+- No pooler output included, because loading `MegatronBertModel` from this MLM checkpoint would introduce randomly initialized pooler weights.