Update README.md

README.md

@@ -20,4 +20,37 @@ KeyError: 'qwen2'
 ```
 
 ## Usage
-The 'lm_head' layer of this model has been removed, which means it can be used for embeddings. It will not perform greatly, as it needs to be further fine-tuned, as shown by [intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct).
+The 'lm_head' layer of this model has been removed, which means it can be used for embeddings. It will not perform greatly, as it needs to be further fine-tuned, as shown by [intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct).
+
+## Inference
+```python
+from sentence_transformers import SentenceTransformer
+import torch
+
+# 1. Load a pretrained Sentence Transformer model
+model = SentenceTransformer("ssmits/Qwen2-7B-embed-base", device = "cpu")
+
+# The sentences to encode
+sentences = [
+    "The weather is lovely today.",
+    "It's so sunny outside!",
+    "He drove to the stadium.",
+]
+
+# 2. Calculate embeddings by calling model.encode()
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# (3, 3584)
+
+# 3. Calculate the embedding similarities
+# Assuming embeddings is a numpy array, convert it to a torch tensor
+embeddings_tensor = torch.tensor(embeddings)
+
+# Using torch to compute cosine similarity matrix
+similarities = torch.nn.functional.cosine_similarity(embeddings_tensor.unsqueeze(0), embeddings_tensor.unsqueeze(1), dim=2)
+
+print(similarities)
+# tensor([[1.0000, 0.8735, 0.7051],
+#         [0.8735, 1.0000, 0.7199],
+#         [0.7051, 0.7199, 1.0000]])
+```