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  "author": "Sebastian Raschka / Ahead of AI",
  "title_listed": "Developing an LLM: Building, Training, Finetuning",
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  "url": "https://magazine.sebastianraschka.com/p/llms-building-training-finetuning",
  "publish_date": "2024-08",
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  "notes": "LLM全生命周期开发指南，从架构到部署",
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  "title": "Developing an LLM: Building, Training, Finetuning",
  "body_markdown": "If your weekend plans include catching up on AI developments and understanding Large Language Models (LLMs), I've prepared a 1-hour presentation on the development cycle of LLMs, covering everything from architectural implementation to the finetuning stages.\nThe presentation also includes an overview and discussion of the different ways LLMs are evaluated, along with the caveats of each method.\nBelow, you'll find a table of contents to get an idea of what this video covers (the video itself has clickable chapter marks, allowing you to jump directly to topics of interest):\n00:00 – Using LLMs\n02:50 – The stages of developing an LLM\n05:26 – The dataset\n10:15 – Generating multi-word outputs\n12:30 – Tokenization\n15:35 – Pretraining datasets\n21:53 – LLM architecture\n27:20 – Pretraining\n35:21 – Classification finetuning\n39:48 – Instruction finetuning\n43:06 – Preference finetuning\n46:04 – Evaluating LLMs\n53:59 – Pretraining & finetuning rules of thumb\nIt's a slight departure from my usual text-based content, but if you find this format useful and informative, I might occasionally create and share more of them in the future.\nHappy viewing!\nAhead of AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.\nSubscribe",
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