Update README.md

README.md

@@ -97,6 +97,49 @@ embeddings = model_ctx.encode(doc_chunks)
 
 </details>
 
+<details>
+<summary> Using ONNX models </summary>
+
+```python
+
+import onnxruntime as ort
+from transformers import AutoTokenizer
+import numpy as np
+
+tokenizer = AutoTokenizer.from_pretrained("perplexity-ai/pplx-embed-v1-0.6b", trust_remote_code=True)
+session = ort.InferenceSession("onnx/model.onnx")
+
+
+texts = [
+    "Scientists explore the universe driven by curiosity.",
+    "Children learn through curious exploration.",
+    "Historical discoveries began with curious questions.",
+    "Animals use curiosity to adapt and survive.",
+    "Philosophy examines the nature of curiosity.",
+]
+
+tokenized = tokenizer(
+    texts,
+    padding=True,
+    truncation=True,
+    return_tensors="np"
+)
+
+onnx_inputs = {
+    "input_ids": tokenized["input_ids"].astype(np.int64),
+    "attention_mask": tokenized["attention_mask"].astype(np.int64),
+}
+
+# Run inference
+onnx_embeddings = session.run([out.name for out in session.get_outputs()], onnx_inputs)
+
+# ONNX produces both int8 and binary precision embeddings:
+int8_embeddings = onnx_embeddings[2]
+binary_embeddings = onnx_embeddings[3]
+```
+
+</details>
+
 ## Technical Details
 
 For comprehensive technical details and evaluation results, see our paper on arXiv.