Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Model and Tokenizer Setup
+model_name = "unsloth/gemma-3-4b-it-unsloth-bnb-4bit"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    load_in_4bit=True,
+    device_map="auto",
+    torch_dtype=torch.bfloat16, #important for speed.
+)
+
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=256) # Adjust max_new_tokens as needed
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your prompt here..."),
+    outputs=gr.Textbox(),
+    title="Gemma 3-4B Inference",
+    description="Run the unsloth/gemma-3-4b-it-unsloth-bnb-4bit model.",
+)
+
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860) #important for spaces.