Create README.md

README.md

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+---
+license: mit
+base_model:
+- Qwen/Qwen3-Embedding-0.6B
+---
+
+onnx version.
+
+
+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