@Fluyeporlaweb: This genius published a step-by-step guide on GitHub for building and training your own model from scratch. No magic. N…
Summary
A GitHub guide published by Fluyeporlaweb shows how to build and train a Transformer model from scratch, implementing attention, multi-head, embeddings, and post-training algorithms (SFT, PPO, DPO, GRPO) without high-level libraries, trained on The Pile dataset.
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This genius published a step-by-step guide on GitHub for building and training your own model from scratch.
No magic.
No libraries that hide the process.
No paying for a datacenter.
5.7k stars. 760 forks. MIT.
Full Transformer implemented following “Attention is All You Need” - attention, multi-head, embeddings, all done by hand
Tokenization with tiktoken over 825GB of The Pile
Trains from 13M up to over 2B parameters on a single GPU
With a free Colab T4, you already have your own trained model
Hardware table: how much GPU you need for each size
Post-training suite from scratch: SFT, Reward Model, PPO, DPO, and GRPO
No trl, no peft, no transformers - every algorithm written by hand
Real datasets: Alpaca, Dolly, Anthropic HH-RLHF, UltraFeedback, GSM8K
Multi-GPU with DDP and bf16
The 13M parameter model trained on the free T4 generates sentences with perfect grammatical structure and zero sense.
And there’s the lesson.
You see with your own eyes the exact line between what’s free and what needs real resources.
the link
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