I built LLMs-from-scratch - a complete guide to implementing GPT from the ground up
Two years ago, I wanted to truly understand how LLMs work under the hood. Every tutorial just showed me how to call an API. So I built something different.
LLMs-from-scratch (88K ⭐ on GitHub) is a complete, step-by-step implementation of a GPT-like LLM in pure PyTorch - no HuggingFace, no external libraries. You build every component: attention mechanisms, tokenizers, transformer blocks, training loops.
What makes it unique: - Runs on a laptop (no GPU farm needed) - Covers pretraining AND finetuning - Includes LoRA, DPO, and instruction tuning - 17-hour video course available
The book "Build a Large Language Model (From Scratch)" just hit #1 on Amazon's AI category. But the GitHub repo stands alone as a free, comprehensive resource.
Would love feedback from anyone who's gone through it. What's the hardest part for beginners?
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I built LLMs-from-scratch - a complete guide to implementing GPT from the ground up
Two years ago, I wanted to truly understand how LLMs work under the hood. Every tutorial just showed me how to call an API. So I built something different.
LLMs-from-scratch (88K ⭐ on GitHub) is a complete, step-by-step implementation of a GPT-like LLM in pure PyTorch - no HuggingFace, no external libraries. You build every component: attention mechanisms, tokenizers, transformer blocks, training loops.
What makes it unique:
- Runs on a laptop (no GPU farm needed)
- Covers pretraining AND finetuning
- Includes LoRA, DPO, and instruction tuning
- 17-hour video course available
The book "Build a Large Language Model (From Scratch)" just hit #1 on Amazon's AI category. But the GitHub repo stands alone as a free, comprehensive resource.
Would love feedback from anyone who's gone through it. What's the hardest part for beginners?
4 months ago | [YT] | 0
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