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README.md

@@ -64,37 +64,18 @@ language:
 
 ## 사용 방법
 ```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
-
-# 모델 로드
-BASE_MODEL = "skt/kogpt2-base-v2"
-LORA_MODEL = "your-username/lora_koqpt2_chatbot"
-
-tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
-base_model = AutoModelForCausalLM.from_pretrained(BASE_MODEL)
-model = PeftModel.from_pretrained(base_model, LORA_MODEL)
-model.eval()
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model.to(device)
-
-# 텍스트 생성
-prompt = "안녕하세요"
-inputs = tokenizer(prompt, return_tensors="pt").to(device)
-
-with torch.no_grad():
-    outputs = model.generate(
-        **inputs,
-        max_length=100,
-        temperature=0.7,
-        top_p=0.9,
-        do_sample=True
-    )
-    
-generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(generated_text)
+# 1. 토크나이저 불러오기
+tokenizer_reload = AutoTokenizer.from_pretrained(model_name_upload)
+
+# 2. 베이스 모델 불러오기
+base_reload = AutoModelForCausalLM.from_pretrained("skt/kogpt2-base-v2")
+
+# 3. 학습할 때와 똑같이 크기 조정!
+base_reload.resize_token_embeddings(len(tokenizer_reload))
+base_reload.config.pad_token_id = tokenizer_reload.pad_token_id
+
+# 4. LoRA 불러오기
+model_reload = PeftModel.from_pretrained(base_reload, model_name_upload)
 ```
 
 ## 특징