Create app.py

app.py

@@ -0,0 +1,29 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM 
+
+tokenizer = AutoTokenizer.from_pretrained(
+  'kakaobrain/kogpt', revision='KoGPT6B-ryan1.5b',
+  bos_token='[BOS]', eos_token='[EOS]', unk_token='[UNK]', pad_token='[PAD]', mask_token='[MASK]'
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+  'kakaobrain/kogpt', revision='KoGPT6B-ryan1.5b',
+  pad_token_id=tokenizer.eos_token_id,
+  torch_dtype=torch.float16, low_cpu_mem_usage=True
+).to(device='cpu', non_blocking=True)
+_ = model.eval()
+
+
+
+def greet(text):
+    prompt = text
+    with torch.no_grad():
+      tokens = tokenizer.encode(prompt, return_tensors='pt').to(device='cpu', non_blocking=True)
+      gen_tokens = model.generate(tokens, do_sample=True, temperature=0.8, max_length=64)
+      generated = tokenizer.batch_decode(gen_tokens)[0]
+      
+    return generated
+    
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()