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diff --git a/tf.py b/tf.py
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+
+import torch
+import torch.nn.functional as F
+
+from torch import Tensor
+from transformers import AutoModel, AutoTokenizer
+
+
+def last_token_pool(last_hidden_states: Tensor,
+                 attention_mask: Tensor) -> Tensor:
+    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
+    if left_padding:
+        return last_hidden_states[:, -1]
+    else:
+        sequence_lengths = attention_mask.sum(dim=1) - 1
+        batch_size = last_hidden_states.shape[0]
+        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
+
+
+def get_detailed_instruct(task_description: str, query: str) -> str:
+    return f'Instruct: {task_description}\nQuery:{query}'
+
+# Each query must come with a one-sentence instruction that describes the task
+task = 'Given a web search query, retrieve relevant passages that answer the query'
+
+queries = [
+    get_detailed_instruct(task, 'What is the capital of China?'),
+    get_detailed_instruct(task, 'Explain gravity')
+]
+# No need to add instruction for retrieval documents
+documents = [
+    "The capital of China is Beijing.",
+    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
+]
+input_texts = queries + documents
+
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving,
+# together with setting `padding_side` to "left":
+model = AutoModel.from_pretrained(
+    "Qwen/Qwen3-Embedding-8B", attn_implementation="flash_attention_2", device_map="auto", torch_dtype=torch.float16
+)
+tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-Embedding-8B', padding_side="left")
+# The queries and documents to embed
+max_length = 8192
+
+# Tokenize the input texts
+batch_dict = tokenizer(
+    input_texts,
+    padding=True,
+    truncation=True,
+    max_length=max_length,
+    return_tensors="pt",
+)
+batch_dict.to(model.device)
+with torch.no_grad():
+    outputs = model(**batch_dict)
+    embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+
+    # normalize embeddings
+    embeddings = F.normalize(embeddings, p=2, dim=1)
+    scores = (embeddings[:2] @ embeddings[2:].T)
+
+print(scores.tolist())
+# [[0.7645568251609802, 0.14142508804798126], [0.13549736142158508, 0.5999549627304077]]