Update README.md

README.md

@@ -55,15 +55,19 @@ Checkpoints format: `transformers` (Megatron-DeepSpeed format available [here](h
 
 ```python
 import torch
+from peft import PeftModel, PeftConfig
 from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("llm-jp/[Model_Name]")
-model = AutoModelForCausalLM.from_pretrained("llm-jp/[Model_Name]", torch_dtype=torch.float16)
+peft_model_name = "llm-jp/llm-jp-13b-instruct-lora-jaster-v1.0"
+tokenizer = AutoTokenizer.from_pretrained(peft_model_name)
+config = PeftConfig.from_pretrained(peft_model_name)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, device_map="auto", torch_dtype=torch.float16)
+model = PeftModel.from_pretrained(model, peft_model_name)
 text = "自然言語処理とは何か"
 text = text + "### 回答："
-tokenized_input = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").to(model.device)
+tokenized_input = tokenizer(text, add_special_tokens=False, return_tensors="pt").to(model.device)
 with torch.no_grad():
     output = model.generate(
-        tokenized_input,
+        **tokenized_input,
         max_new_tokens=100,
         do_sample=True,
         top_p=0.95,