huggingfaceのTrainerクラスを使えばFineTuningの学習コードがスッキリ書けてめちゃくちゃ便利です - Qiita
もし明日、上司に「GPT-4を作れ」と言われたら？ Stability AIのシニアリサーチサイエンティストが紹介する「LLM構築タイムアタック」
【速報】OpenAI APIでGPT-3.5-turboがfine-tuningできるようになりました！！ | DevelopersIO
ただし、GPT-4と比較すると安価にはなっていますので、性能比較をしながら良い塩梅を探すとコスト的にメリットがある可能性があります。 また学習のコストですが、以下のように学習データ全体のトークン数 x エポック数という形で費用が掛かるので注意が必要です。 （エポック数は後述するようにパラメータとして指定可能です） For example, a gpt-3.5-turbo fine-tuning job with a training file of 100,000 tokens that is trained for 3 epochs would have an expected cost of $2.40. 公式ページは以下となりますので、併せてご確認ください。 レート制限 fine-tuningのレート制限については、以下に記載があります。 fine-tuningについては以下のように
LLMを効率的に再学習する手法(PEFT)を解説 - Platinum Data Blog by BrainPad
GoogleAdSenseの遅延読込みでページ表示速度を改善
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Linux distributions still ship with the assumption that they will be multi-user systems, meaning resource limits are set for a normal human doing day-to-day desktop work. For a high-performance system trying to serve thousands of concurrent network clients, these limits are far too low. If you have an online game or web app that’s pushing the envelope, these settings can help increase awesomeness.
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https://rubytune.com/cheat/
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