Update README.md

README.md

@@ -51,25 +51,32 @@ from trl import SFTTrainer
 # 各自HugginFaceのトークンを取得してください
 HF_TOKEN = "your-token"
 
-model_name = "ryomac/llm-jp-3-13b-ry-ft1"
+model_id = "llm-jp/llm-jp-3-13b"
+adapter_id =  "ryomac/llm-jp-3-13b-ry-ft1"
 
-# QLoRA config
+
+# QLoRA用の設定
 bnb_config = BitsAndBytesConfig(
     load_in_4bit=True,
     bnb_4bit_quant_type="nf4",
     bnb_4bit_compute_dtype=torch.bfloat16,
 )
 
-# Load model
+
+# モデル読み込み
 model = AutoModelForCausalLM.from_pretrained(
-    model_name,
+    model_id,
     quantization_config=bnb_config,
     device_map="auto",
-    token = HF_TOKEN
+    token=HF_TOKEN
 )
 
-# Load tokenizer
-tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, token = HF_TOKEN)
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token=HF_TOKEN)
+
+# Peftモデルを適用
+model = PeftModel.from_pretrained(model, adapter_id, token=HF_TOKEN)
+
+
 # データセットの読み込み。
 datasets = []
 with open("./elyza-tasks-100-TV_0.jsonl", "r") as f: