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app.py

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+import torch
+import gradio as gr
+
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+peft_model_id = "kimmeoungjun/qlora-koalpaca2"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
+model = PeftModel.from_pretrained(model, peft_model_id).to(device)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+def generate(q):
+    inputs = tokenizer(f"### 질문: {q}\n\n### 답변:", return_tensors='pt', return_token_type_ids=False)
+    outputs = model.generate(
+        **{k: v.to(device) for k, v in inputs.items()},
+        max_new_tokens=256,
+        do_sample=True,
+        eos_token_id=2,
+    )
+    result = tokenizer.decode(outputs[0])
+    answer_idx = result.find("### 답변:")
+    answer = result[answer_idx + 7:].strip()
+    return answer
+
+gr.Interface(generate, "text", "text").launch(share=True)

requirements.txt

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+torch
+transformers
+peft