Update README.md

README.md

@@ -24,13 +24,17 @@ language:
 # 使用方法例
 
 1. 依存関係のインストール:
-```
-bash !pip install transformers datasets unsloth
+```bash
+# 必要なライブラリをインストール
+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
 ```
 
 2. モデルの読み込み:
 
-```python:
+```python
 from unsloth import FastLanguageModel
 from peft import PeftModel
 
@@ -51,17 +55,50 @@ model = PeftModel.from_pretrained(model, lola_adapter_id, token=hf_token)
 ```
 3. 推論
 
-```
+
+```python
 FastLanguageModel.for_inference(model)
 
 inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
 
 outputs = model.generate(**inputs, max_new_tokens = max_token, use_cache = True, temperature=0.5, top_p=0.9,do_sample=False, repetition_penalty=1.2)
 
-prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 ```
 
+4. 評価データの読み込みと評価
+
+```python
+import pandas as pd
+from datasets import Dataset
+
+datasets = []
+file_path = <評価データ（jsonl)>のパス
+
+df = pd.read_json(filename, orient='records', lines=True)
+
+results = []
+
+for r in iterrows():
+  imput = r["input"]
+  prompt = f"""### 指示\n{input} 簡潔に回答してください \n### 回答\n"""
+
+  inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
+
+  outputs = model.generate(**inputs, max_new_tokens = max_token, use_cache = True, temperature=0.5, top_p=0.9,do_sample=False, repetition_penalty=1.2)
+  prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]
+
+  results.append({"task_id": r["task_id"], "input": input, "output": prediction})
+
+# 結果をjsonlで保存。
+json_file_id = <保存ファイル名>
+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')
+
+```
 
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.