Update README.md

README.md

@@ -27,20 +27,24 @@ This llama model was trained 2x faster with [Unsloth](https://github.com/unsloth
 '''
 python
 ## 必要パッケージのインストール
+```
 %%capture
 !pip install unsloth
 !pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
 !pip install -U torch
 !pip install -U peft
-
-
+```
+## ライブラリの読み込み
+```
 from unsloth import FastLanguageModel
 from peft import PeftModel
 import torch
 import json
 from tqdm import tqdm
 import re
-
+```
+## モデル、トークナイザーの読み込み
+```
 model_id = "llm-jp/llm-jp-3-13b"
 adapter_id = "TKKKMMM/llm-jp-3-13b-it_lora"
 
@@ -49,16 +53,19 @@ HF_TOKEN = "YOURE TOKEN"
 dtype = None 
 load_in_4bit = True 
 
-## モデル、トークナイザーの読み込み
 model, tokenizer = FastLanguageModel.from_pretrained(
     model_name=model_id,
     dtype=dtype,
     load_in_4bit=load_in_4bit,
     trust_remote_code=True,
 )
+```
 ## 元のモデルにLoRAのアダプタを統合。
+```
 model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
+```
 ## データセット読み込み
+```
 datasets = []
 with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
     item = ""
@@ -68,8 +75,9 @@ with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
       if item.endswith("}"):
         datasets.append(json.loads(item))
         item = ""
-
+```
 ## 回答の生成と格納
+```
 FastLanguageModel.for_inference(model)
 
 results = []
@@ -86,13 +94,14 @@ for dt in tqdm(datasets):
   prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]
 
   results.append({"task_id": dt["task_id"], "input": input, "output": prediction})
-  
+```
 ## jsonファイルへのエクスポート
+```
 json_file_id = re.sub(".*/", "", adapter_id)
 with open(f"/content/{json_file_id}_output.jsonl", 'w', encoding='utf-8') as f:
     for result in results:
         json.dump(result, f, ensure_ascii=False)
         f.write('\n')
-'''
+```