Create gemma_model.py

gemma_model.py

@@ -0,0 +1,10 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "google/gemma-2b-it"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_text(prompt):
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=50)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)