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HayatoHongo/EveryonesLLM

62MITJupyter Notebook

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Stars: 510
Forks: 89
Watchers: 2
Open issues: 0
Contributors: 2

Star history, last 90 days+1 over 3 snapshots

2026-07-31 · 5092026-08-06 · 510

Star history, last 90 days
Date	Stars
2026-07-31	509
2026-08-04	510
2026-08-06	510

Today

— stars today

This week

— stars this week

This month

— stars this month

Momentum

0.0

growth rate 0.00%/day

From the README

🌐 Select Language / 日本語 🇯🇵 | 中文 🇨🇳

Build LLM on Google Colab from scratch

Click-> AI YOU build in Chapter29😘

WebApp Released (Now only in Japanese)

EveryonesLLM

Chapter	Estimated Time	Notebook
Chapter 00: Start Tutorial	1-2 hours
Chapter 01: Dataloader	1-2 hours
Chapter 02: TokenEmbedding	0.5-1 hour
Chapter 03: PositionEmbedding	0.5-1 hour
Chapter 04: EmbeddingModule	0.5-1 hour
Chapter 05: LayerNorm	1-2 hours
Chapter 06: AttentionHead	3-4 hours
Chapter 07: MultiHeadAttention	1-2 hours
Chapter 08: FeedForward	1-2 hours
Chapter 09: TransformerBlock	0.5-1 hour
Chapter 10: VocabularyLogits	0.5-1 hour
Chapter 11: nanoGPT	1-2 hours
Chapter 12: Trainer	1-2 hours
Chapter 13: Tokens per second(CPU)	1-2 hours
Chapter 14: Tokens per second(T4 GPU)	0.5-1 hour
Chapter 15: Train nanoGPT with GPU	0.5-1 hour
Chapter 16: Make only the model size bigger	0.5-1 hour (+ 1 hour model training)
Chapter 17: Make the dataset bigger	1-2 hours (+ 1 hour model training)
Chapter 18: tiktoken	1-2 hours (+ 1 hour model training)
Chapter 19: Long Train	1-2 hours (+ 6 hours model training)
Chapter 20: Learning rate	0.5-1 hour
Chapter 21: Scaling Law	1-2 hours
Chapter 22: TinyStories(Main)	1-2 hours
Chapter 22: TinyStories(Model Training)	1 hour
Chapter 23: RPE(OverSimplified)	2-3 hours
Chapter 24: RPE(Simplified)	1-2 hours (+ 1 hour model training)
Chapter 25: LR schedule	1 hour
Chapter 26: Checkpoint	1 hour
Chapter 27: Pretraining	0.5 hour (+ 20 hours model training)
Chapter 28: Instruction Tuning	0.5 hour (+ 0.5 hour model training)
Chapter 29: Magpie (Prompt mask)	1.5 hours (+ 2 hours model training)

2026/6/5 Vision LLM beta is now available!

Explanations and exercises are not available yet. Evaluation on major benchmarks is also not available yet.

Please use it for early preview learning. We plan to update it from time to time, so we recommend working on it after future updates.

Chapter	Estimated time	Notebook
Chapter 30: Vision Pretraining (Beta)	3 hours model training
Chapter 31: Vision Instruction Tuning (Beta)	2 hours model training

Link to Web App (Vision LLM)

Tensor Map (Full Tensor Overview)

Try making the tensor map below by yourself!
Do not worry, I prepared lots of hints for you.
View the full-resolution Tensor Map of the nanoGPT model on Canva

About the Development Environment

To keep setup easy, please try running all the samples on Google Colab.

However, Google Colab does not save checkmarks in checkboxes.
If you want to track your progress, or if you want to work little by little, say every 30 minutes, I recommend VS Code.
In that case, fork this repository and clone it to your own PC. Just use Google Colab extension for your VS code, then you can use Colab CPU and GPU.

Answers

Chapter	Estimated Time	Notebook
Chapter 00: Start Tutorial	1-2 hours
Chapter 01: Dataloader	1-2 hours
Chapter 02: TokenEmbedding	0.5-1 hour
Chapter 03: PositionEmbedding	0.5-1 hour
Chapter 04: EmbeddingModule	0.5-1 hour
Chapter 05: LayerNorm	1-2 hours
Chapter 06: AttentionHead	3-4 hours
Chapter 07: MultiHeadAttention	1-2 hours
Chapter 08: FeedForward	1-2 hours
Chapter 09: TransformerBlock	0.5-1 hour
Chapter 10: VocabularyLogits	0.5-1 hour
Chapter 11: nanoGPT	1-2 hours
Chapter 12: Trainer	1-2 hours
Chapter 13: Tokens per second(CPU)	1-2 hours
Chapter 14: Tokens per second(T4 GPU)	0.5-1 hour
Chapter 15: Train nanoGPT with GPU	0.5-1 hour
Chapter 16: Make only the model size bigger	0.5-1 hour (+ 1 hour model training)
Chapter 17: Make the dataset bigger	1-2 hours (+ 1 hour model training)
Chapter 18: tiktoken	1-2 hours (+ 1 hour model training)
Chapter 19: Long Train	1-2 hours (+ 6 hours model training)
Chapter 20: Learning rate	0.5-1 hour
Chapter 21: Scaling Law	1-2 hours
Chapter 22: TinyStories(Main)	1-2 hours
Chapter 22: TinyStories(Model Training)	1 hour
Chapter 23: RPE(OverSimplified)	2-3 hours
Chapter 24: RPE(Simplified)	1-2 hours (+ 1 hour model training)
Chapter 25: LR schedule	1 hour
Chapter 26: Checkpoint	1 hour
Chapter 27: Pretraining	0.5 hour (+ 20 hours model training)
Chapter 28: Instruction Tuning	0.5 hour (+ 1 hour model training)
Chapter 29: Magpie (Prompt mask)	1.5 hours (+ 2 hours model training)

Sources

This tutorial is based on Andrej Karpathy's nanoGPT and jingyaogong's Minimind. For Instruction Tuning, it refers to Sebastian Raschka's book Build a Large Language Model (From Scratch). For Vision LLM, it refers to LLaVA. I would like to take this opportunity to express my sincere gratitude.

Notice

This project is a community-based open-source educational project and is not affiliated with Google in any way.

About Project EveryonesLLM

Rendered by GitHub from the current README

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Top contributors2 shown

HayatoHongo
108
newbeeR2020
4

Quality score

Would you bet a product on this? Bounded 0–100 and slow moving.

62/10062

Maintenance: 70%; How recently the default branch was pushed to
Releases: 25%; Recency and cadence of tagged releases
Community: 9%; Contributor breadth and bus factor
Issue backlog: 100%; Open issues measured against the size of the audience
Documentation: 88%; README depth, plus a homepage and topics
Licensing: 100%; How freely the code can be adopted

What to know before adopting

No tagged releases
Bus factor of 1 — one contributor dominates
Usable in commercial products with attribution.

Details

Created: last year
Last push: last month
Last release: none
Release tag: —
Releases in the last year: 0
Default branch: main
Size: 230k KB
Owner type: User
Owner location: Tokyo
Discovered via: phrase:llm
First indexed: 7 days ago
Last synced: 2 days ago