@yibie: Recommend this repo to build a GPT-style transformer from scratch without any advanced libraries. With 13M parameters, it can produce grammatically correct text, trainable in one day on a free Colab T4. Train your own LLM from scratch: 13M parameter GPT implementation - Akshay shares…
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Recommended a GitHub repo for building a GPT-style Transformer from scratch without advanced libraries. With 13M parameters, it can be trained in one day on free Colab to generate grammatically correct text.
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Recommend this repo: build a GPT-style transformer from scratch without any high-level libraries. With 13M parameters it can produce grammatically correct text, trainable in one day on a free Colab T4.
Train your own LLM from scratch: a 13M parameter GPT implementation
Repo shared by Akshay, covering the full pipeline from data download to text generation: • Uses The Pile dataset (825GB) • tiktoken tokenizer • Complete training loop (eval, LR decay, checkpoint) • Includes SFT and RLHF guide
With 13M parameters you get correct grammar and spelling, trainable in one day on a free Colab T4.
Original: https://x.com/akshay_pachaar/status/2066551571031458086…
#LLM #TrainFromScratch #AI
Akshay 🚀 (@akshay_pachaar): Train your own LLM from scratch.
This repo builds a GPT-style transformer from the ground up, without using any high-level libraries.
You see exactly how attention, multi-head attention, the feed-forward block, embeddings, residuals, and layer norm fit together.
And it doesn’t
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