Update README.md

README.md

@@ -37,24 +37,33 @@ The inference can be done using the following snippet of code:
 ```python
 import transformers
 
-model_id = ("cjvt/GaMS-1B-Chat")
-pipeline = transformers.pipeline(
-        "text-generation",
-        model=model_id,
-        device_map="auto"
-    )
-
-# Example of response generation
-message = [{"role": "user", "content": "Kateri je najpomembnejši dogodek v slovenski zgodovini?"}]
-response = pipeline(message, max_length=100)
-print("Model's response:", response[0]["generated_text"][-1]["content"])
-
-# Example of conversation chain
-new_message = response[0]["generated_text"]
-new_message.append({"role": "user", "content": "Lahko bolj podrobno opišeš ta dogodek?"})
-response = pipeline(new_message, max_length=500)
-print("Model's response:", response[0]["generated_text"][-1]["content"])
+model_id = ("cjvt/GaMS-1B")
+
+pline = pipeline(
+    "text-generation",
+    model=model_id,
+    device_map="auto"
+)
+
+prompts = [
+    "The examples of antonyms are:\nhigh => low\nwide => narrow\nbig =>",
+    "Pristanek je bil prvi nadzorovani spust ameriškega vesoljskega plovila na površje Lune po Apollu 17 leta 1972, ko je na Luni pristala zadnja Nasina misija s posadko.\nDoslej so na Luni pristala vesoljska plovila le iz štirih drugih držav –",
+    "U četvrtak je bila prva polufinalna večer Dore, a komentari na društvenim mrežama ne prestaju. U nedjeljno finale prošli su:"
+]
+
+sequences = pline(
+    prompts,
+    max_length=1000,
+    do_sample=False,
+    num_return_sequences=1
+)
+
+for seq in sequences:
+    print("--------------------------")
+    print(f"Result: {seq[0]['generated_text']}")
+    print("--------------------------\n")
 ```
+
 ## Training details
 
 ### Training data