Update README.md

README.md

@@ -19,3 +19,21 @@ We present the evaluation results viusally in Figure 1.
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b080b373329eb145daa150/Cl0S2V51aucdo0IMDTCSy.png)
 
 **Figure 1: Model Performance Comparison Across Evaluation Bencmarks**
+
+You can run inference with VezilkaLLM using the Hugging Face Transformers library as shown below:
+
+```{python}
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+model_id = "finki-ukim/VezilkaLLM"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+prompt = "Зборот „пријателство“ значи "
+outputs = generator(prompt, max_new_tokens=128, do_sample=True, temperature=0.7)
+
+print(outputs[0]["generated_text"])
+```