Update README.md

README.md

@@ -55,4 +55,39 @@ print(similarities)
 #         [0.6609, 0.7046, 1.0000]])
 ```
 
-Note: In my tests it utilizes more than 24GB (RTX 4090), so an A100 or A6000 would be required for inference.
+Note: In my tests it utilizes more than 24GB (RTX 4090), so an A100 or A6000 would be required for inference.
+
+## Inference (HuggingFace Transformers)
+Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('ssmits/Qwen2-7B-Instruct-embed-base')
+model = AutoModel.from_pretrained('ssmits/Qwen2-7B-Instruct-embed-base') # device = "cpu" when <= 24 GB VRAM
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```