---
license: mit
base_model:
- Qwen/Qwen3-Embedding-0.6B
---

interface sample:

```
import numpy as np
import onnxruntime as ort
from transformers import AutoTokenizer


class QwenEmbeddingONNX:
    def __init__(self, model_dir, model_path, max_length=512, providers=None):
        self.max_length = max_length
        self.tokenizer = AutoTokenizer.from_pretrained(model_dir)

        if providers is None:
            providers = ['CPUExecutionProvider']

        self.session = ort.InferenceSession(
            model_path,
            providers=providers
        )

        self.input_names = [i.name for i in self.session.get_inputs()]

    def encode(self, texts, normalize=True):
        if isinstance(texts, str):
            texts = [texts]

        inputs = self.tokenizer(
            texts,
            padding=True,
            truncation=True,
            max_length=self.max_length,
            return_tensors="np",
        )

        input_feed = {
            k: inputs[k].astype(np.int64)
            for k in self.input_names
            if k in inputs
        }

        outputs = self.session.run(None, input_feed)
        embeddings = outputs[0]

        # 如果输出是 [B, L, H]，取第一个 token
        if embeddings.ndim == 3:
            embeddings = embeddings[:, 0, :]

        # L2 normalize
        if normalize:
            norm = np.linalg.norm(embeddings, axis=1, keepdims=True)
            embeddings = embeddings / (norm + 1e-9)

        return embeddings
```