Update README.md

README.md

@@ -2,199 +2,122 @@
 library_name: transformers
 tags:
 - unsloth
+license: gemma
+datasets:
+- llm-jp/magpie-sft-v1.0
+- DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k
+- weblab-GENIAC/Open-Platypus-Japanese-masked
+base_model:
+- google/gemma-2-27b
 ---
 
-# Model Card for Model ID
+## 学習データ
+以下のデータセットを使用。
+- [llm-jp/magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0) (apache-2.0)
+- [DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k](https://huggingface.co/datasets/DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k) (apache-2.0)
+- [weblab-GENIAC/Open-Platypus-Japanese-masked](https://huggingface.co/datasets/weblab-GENIAC/Open-Platypus-Japanese-masked) (MIT)
+  - MITライセンスのデータのみ抽出して使用。
+ 
+gemma-2利用にあたり、ライセンス上制約の懸念のあるデータセットは利用していない。
+
+## 推論手順
+unsloth版のサンプルコード（Google Colab L4使用）をベースとし、推論は１時間以内で終了するようになっている。
+なお、unsloth版でgemmaを直接使用しようとすると、意図せず別のモデルがダウンロードされることが報告されていることから、当該事象を回避するため、ローカルに一度ダウンロードしたものを使用する形に変更している。
+
+```
+# 必要なライブラリをインストール
+%%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
+```
+
+```
+HF_TOKEN = "" #必要なトークンを設定してください
+```
+
+```
+# HFからモデルリポジトリをダウンロード
+!huggingface-cli login --token $HF_TOKEN
+!huggingface-cli download google/gemma-2-27b --local-dir gemma-2-27b/
+```
+
+```
+# 必要なライブラリを読み込み
+from unsloth import FastLanguageModel
+from peft import PeftModel
+import torch
+import json
+from tqdm import tqdm
+import re
+```
+
+```
+# ベースとなるモデルと学習したLoRAのアダプタ
+model_id = "/content/gemma-2-27b"
+adapter_id = "Taka2024/gemma-2-27b-it-2_lora"
+```
+
+```
+# unslothのFastLanguageModelで元のモデルをロード。
+dtype = None # Noneにしておけば自動で設定
+load_in_4bit = True # 今回は27Bモデルを扱うためTrue
+
+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)
+```
+
+```
+# タスクとなるデータの読み込み。
+# 事前にデータをアップロードしてください。
+datasets = []
+with open("/content/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 = ""
+```
+
+```
+# モデルを用いてタスクの推論。
+# 推論するためにモデルのモードを変更
+FastLanguageModel.for_inference(model)
+
+results = []
+for dt in tqdm(datasets):
+  input = dt["input"]
+
+  prompt = f"""### あなたは日本人のための優秀なコンシェルジュです。指示には必ずわかりやすい日本語で回答してください。\n### 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})
+```
+
+```
+# 結果をjsonlで保存。
+
+# ここではadapter_idを元にファイル名を決定しているが、ファイル名は任意で問題なし。
+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')
+```
 
-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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