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{
"cells": [
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"source": [
"### 仮想環境の構築\n",
"python ver 3.9.6 \n",
"名称は「fine_tuning_with_clm」 \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ソースからのHuggingface Transformersのインストール\n",
"# !pip install requirements.txt\n",
"# !pip install numpy --pre torch torchvision torchaudio --force-reinstall --index-url https://download.pytorch.org/whl/nightly/cu117\n",
"!git clone https://github.com/huggingface/transformers -b v4.28.1\n",
"# !pip install -e transformers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install -r ./transformers/examples/pytorch/language-modeling/requirements.txt"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"「./transformers/examples/language-modeling/run_clm.py」の編集\n",
"\n",
"```python\n",
"#T5Tokenizerのインポートの追加\n",
"from transformers import T5Tokenizer\n",
"\n",
"#AutoTokenizerをT5Tokenizerに変更\n",
"tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)\n",
" ↓\n",
"tokenizer = T5Tokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)\n",
" ↓\n",
"tokenizer = T5Tokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)\n",
"```"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### fine tuningの実行（jupyter notebook版）\n",
"\n",
"以下のコマンドを実行すれば、このJupyter notebook上でも実行できる。 \n",
"ただし、出力がリアルタイムで更新されず、いまどのぐらい学習が終わったのかわからない。 \n",
"そのため、後述の方法にて、コマンドプロンプトで実行する。 "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# import os\n",
"\n",
"# os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 280minぐらいかかっていたが、終わらなかった\n",
"\n",
"# !python ./transformers/examples/pytorch/language-modeling/run_clm.py \\\n",
"# --model_name_or_path=rinna/japanese-gpt-1b \\\n",
"# --train_file=./train_data/databricks-dolly-15k-ja.json \\\n",
"# --output_dir=output \\\n",
"# --do_train\\\n",
"# --bf16=True \\\n",
"# --tf32=True \\\n",
"# --optim=adafactor \\\n",
"# --num_train_epochs=18 \\\n",
"# --save_steps=384 \\\n",
"# --logging_steps=38 \\\n",
"# --learning_rate=1e-07 \\\n",
"# --lr_scheduler_type=constant \\\n",
"# --gradient_checkpointing \\\n",
"# --per_device_train_batch_size=1 \\\n",
"# --save_safetensors=True \\\n",
"# --logging_dir=logs"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### fine tuningの実行（コマンドプロンプト版）\n",
"\n",
"前提条件として、fine_tunning_with_clm環境をactivateしておくこと。 \n",
"コマンドプロンプトでの仮想環境起動は、Script/activateを実行すればよい。 \n",
"\n",
"※RTX4070で実行してみたところ、学習時間は10時間半だった。 \n",
"\n",
"```cmd\n",
"# 以下はコマンドプロンプトにて実行すること\n",
"\n",
"# RTX4070のみ使用するように指定\n",
"set CUDA_VISIBLE_DEVICES=0\n",
"\n",
"python ./transformers/examples/pytorch/language-modeling/run_clm.py ^\n",
" --model_name_or_path=rinna/japanese-gpt-1b ^\n",
" --train_file=./train_data/databricks-dolly-15k-ja.txt ^\n",
" --output_dir=output ^\n",
" --do_train^\n",
" --bf16=True ^\n",
" --tf32=True ^\n",
" --optim=adafactor ^\n",
" --num_train_epochs=18 ^\n",
" --save_steps=384 ^\n",
" --logging_steps=38 ^\n",
" --learning_rate=1e-07 ^\n",
" --lr_scheduler_type=constant ^\n",
" --gradient_checkpointing ^\n",
" --per_device_train_batch_size=2 ^\n",
" --save_safetensors=True ^\n",
" --logging_dir=logs\n",
"```\n",
"\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## 参考サイト\n",
"\n",
"|サイト|何を参考にしたか|備考| \n",
"|--|--|--| \n",
"|[inu-ai/dolly-japanese-gpt-1b](https://huggingface.co/inu-ai/dolly-japanese-gpt-1b)|学習のハイパーパラメータ|| \n",
"|[Datasets:kunishou/databricks-dolly-15k-ja](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja)|学習に使用したデータ|| \n",
"|[スプラのブキ紹介文を自動生成してみた（GPT）](https://zenn.dev/thr3a/articles/eed434cb20339a)|fine tuning環境構築と実行方法|| \n",
"|[Huggingface Transformers 入門 (28) - rinnaの日本語GPT-2モデルのファインチューニング](https://note.com/npaka/n/n8a435f0c8f69)|fine tuning環境構築と実行方法|| \n",
"|[GPT-2をファインチューニングしてニュース記事のタイトルを条件付きで生成してみた。](https://qiita.com/m__k/items/36875fedf8ad1842b729)|（参考）fine tuning環境構築と実行方法||\n",
"|[Google Colab Proが日本から利用可能に](https://webbigdata.jp/post-9927/)|（参考）|| "
]
}
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