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README.md

@@ -15,35 +15,35 @@ base_model:
 
 #推論時のコード
 
-!pip install -U bitsandbytes
-!pip install -U transformers
-!pip install -U accelerate
-!pip install -U datasets
-!pip install -U peft
-!pip install ipywidgets --upgrade
-
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    BitsAndBytesConfig,
-)
-from peft import PeftModel
-import torch
-from tqdm import tqdm
-import json
+!pip install -U bitsandbytes  
+!pip install -U transformers  
+!pip install -U accelerate  
+!pip install -U datasets  
+!pip install -U peft  
+!pip install ipywidgets --upgrade  
+
+from transformers import (  
+    AutoModelForCausalLM,  
+    AutoTokenizer,  
+    BitsAndBytesConfig,  
+)  
+from peft import PeftModel  
+import torch  
+from tqdm import tqdm  
+import json  
 
 # Hugging Faceで取得したTokenをこちらに貼る。
-HF_TOKEN = "YOUR_HF_TOKEN"
+HF_TOKEN = "YOUR_HF_TOKEN"  
 
-model_id = "llm-jp/llm-jp-3-13b"
-adapter_id = "kiseich/llm-jp-3-13b-Etask"
+model_id = "llm-jp/llm-jp-3-13b"  
+adapter_id = "kiseich/llm-jp-3-13b-Etask"  
 
 # QLoRA config
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.bfloat16,
-)
+bnb_config = BitsAndBytesConfig(  
+    load_in_4bit=True,  
+    bnb_4bit_quant_type="nf4",  
+    bnb_4bit_compute_dtype=torch.bfloat16,  
+)  
 
 # Load model
 model = AutoModelForCausalLM.from_pretrained(