add app.py

app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "AddieFoote0/arithmetic-300M-MaxEnt-distilled-relearned"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+if hasattr(torch, "compile"):
+    model = torch.compile(model)
+    print("compiled model")
+else:
+    print("no compile")
+
+
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=5, temperature=1.0)
+    input_length = inputs['input_ids'].shape[1]
+    new_token_ids = outputs[0][input_length:]
+    new_tokens = tokenizer.decode(new_token_ids, skip_special_tokens=False)
+    return new_tokens
+
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(label="Enter your prompt"),
+    outputs=gr.Textbox(label="Model Response"),
+    title="Arithmetic Model Demo",
+)
+
+iface.launch()