Add README

README.md

@@ -1,3 +1,69 @@
-# モデルの実行方法
+# LLM-JP-3.3.7B-IT LoRA Model
 
-このモデルの使い方は以下の通りです。
+This is the LLM-JP-3.3.7B-IT model fine-tuned with LoRA.
+
+# How to Use
+
+```python
+##################################################
+import torch
+from unsloth import FastLanguageModel
+from peft import PeftModel
+
+HF_TOKEN = ""
+
+# モデルとトークナイザーの読み込み
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="llm-jp/llm-jp-3-3.7b",
+    dtype=None,
+    load_in_4bit=True,
+    trust_remote_code=True,
+)
+model = PeftModel.from_pretrained(model, "nito78/llm-jp-3-3.7b-it_lora_all", token = HF_TOKEN)
+
+# 推論モード
+FastLanguageModel.for_inference(model)
+##################################################
+import json
+
+# データの読み込み
+datasets = []
+with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
+    item = ""
+    for line in f:
+      line = line.strip()
+      item += line
+      if item.endswith("}"):
+        datasets.append(json.loads(item))
+        item = ""
+##################################################
+from tqdm import tqdm
+
+# 推論
+results = []
+for dt in tqdm(datasets):
+  input = dt["input"]
+
+  prompt = f"""### 指示\n{input}\n### 回答\n"""
+
+  inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
+
+  outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, 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": dt["task_id"], "input": input, "output": prediction})
+##################################################
+import re
+import os
+import json
+
+# 保存先ディレクトリ
+output_dir = "./results"
+os.makedirs(output_dir, exist_ok=True)
+
+# 結果を保存
+output_file = os.path.join(output_dir, "result.jsonl")
+with open(output_file, 'w', encoding='utf-8') as f:
+    for result in results:
+        json.dump(result, f, ensure_ascii=False)
+        f.write('\n')