# ONNX exports

Source: [Shuu12121/NightOwl-CodeEmbedding](https://huggingface.co/Shuu12121/NightOwl-CodeEmbedding/tree/79140c0e1db457e70067dc0e8089bf2a094324ac)

| File | Precision |
| --- | --- |
| model.onnx | FP32 |
| model_quantized.onnx | Dynamic INT8 (constant MatMul and Gather weights) |

Inputs: `input_ids` and `attention_mask`, int64 `[batch, sequence]`.
Output: `last_hidden_state`. Take the first token, then L2-normalize to obtain
768-dimensional embeddings. Maximum input: 1024 tokens.
Use the tokenizer/configuration at the repository root. Query/document prefixes are not required.

Node.js short-input example (Transformers.js 4.2.0):

```javascript
import { pipeline } from '@huggingface/transformers';
const extractor = await pipeline('feature-extraction', 'Shuu12121/NightOwl-CodeEmbedding', {
  dtype: 'q8', device: 'cpu',
});
const output = await extractor('parse a JSON string', { pooling: 'cls', normalize: true });
console.log(output.dims); // [1, 768]
await extractor.dispose();
```

For long inputs, truncate the content to 1022 tokens before adding
CLS and SEP. Transformers.js 4.2.0's default truncation slices the final sequence and
can remove SEP; preserve both special tokens for parity with the Python tokenizer.

`manifest.json` records the source commit, export settings, validation results and
SHA256 checksums. These exports use opset 17. The original model's
license and usage terms apply. INT8 output embeddings remain FP32.