# hkproj/pytorch-transformer

Attention is all you need implementation

Repository: https://github.com/hkproj/pytorch-transformer
Canonical: https://ross.abutalabs.com/products/pytorch-transformer
Homepage: https://www.youtube.com/watch?v=ISNdQcPhsts
Language: Jupyter Notebook
License Family: other
Topics: attention-is-all-you-need, paper-implementations, pytorch, transformer
Last push: 2024-06-08T09:12:05+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 85
- inputs: {"age_days": 1203, "days_push": 816, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1271, forks 408 (observed 2026-08-28T04:04:11.945562+00:00)

## What it is
A from-scratch PyTorch implementation of the Transformer architecture from the paper 'Attention Is All You Need'. It accompanies a YouTube video that walks through the full step-by-step implementation, making it a hands-on educational resource rather than a production library.

## Use cases
- learn how the transformer architecture works by coding it from scratch
- implement attention is all you need in pytorch
- understand self-attention and multi-head attention with code
- follow a step-by-step transformer tutorial with a companion video
- study a readable reference implementation of the original transformer paper
- build a seq2seq transformer for machine translation as a learning exercise

## When to choose
- You want to deeply understand the Transformer by implementing it yourself in PyTorch
- You learn best by following along with a video tutorial paired with matching code
- You need a small, readable reference of the original paper's architecture without framework abstractions

## When to avoid
- You need a production-ready, optimized transformer library for training or serving models (use PyTorch or Hugging Face Transformers)
- You need pretrained weights, fine-tuning utilities, or an extensive API
- You need a licensed dependency you can safely embed in your own project - this repo has no license file

## Facets
- artifact type: learning-resource
- maturity: stable
- function: deep-learning, transformers, machine-learning
- domain: deep-learning, machine-learning, tutorials
- platform: python
- tags: pytorch, transformer, self-attention, multi-head-attention, paper-implementation, from-scratch, seq2seq, neural-machine-translation, jupyter-notebook, tutorial-code, educational, natural-language-processing, gpu

## Member repositories
- hkproj/pytorch-transformer (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:11.945562+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:03:21.767574+00:00, confidence not recorded.
  - readme: https://github.com/hkproj/pytorch-transformer (fetched 2026-08-28T04:04:11.945562+00:00, sha 60169868df64)
  - homepage: https://www.youtube.com/watch?v=ISNdQcPhsts (fetched 2026-08-29T12:14:54.064812+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.