# gordicaleksa/pytorch-original-transformer

My implementation of the original transformer model (Vaswani et al.). I've additionally included the playground.py file for visualizing otherwise seemingly hard concepts. Currently included IWSLT pretrained models.

Repository: https://github.com/gordicaleksa/pytorch-original-transformer
Canonical: https://ross.abutalabs.com/products/pytorch-original-transformer
Homepage: https://youtube.com/c/TheAIEpiphany
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: transformer, transformers, pytorch-transformer, pytorch-transformers, attention, attention-mechanism, attention-is-all-you-need, pytorch, python, jupyter, transformer-tutorial, deeplearning, deep-learning, original-transformer
Last push: 2020-12-27T12:27:21+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2144, "days_push": 2075, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1111, forks 188 (observed 2026-08-28T04:03:37.616678+00:00)

## What it is
A PyTorch implementation of the original Transformer model from the 'Attention Is All You Need' paper (Vaswani et al.), designed as a learning resource with well-commented code. It includes a playground.py for visualizing hard-to-grasp concepts like positional encodings and learning rate schedules, plus pretrained IWSLT machine translation models.

## Use cases
- learn how the original transformer architecture works
- visualize positional encodings and attention concepts
- understand the attention is all you need paper with code
- run a pretrained transformer for machine translation
- study a clean pytorch transformer implementation
- teach deep learning concepts like label smoothing and lr schedules

## When to choose
- you want to learn or teach how transformers work from first principles
- you need a readable, well-commented reference implementation rather than a production library
- you want visual explanations of positional encodings and custom learning rate schedules

## When to avoid
- you need state-of-the-art transformer models or modern LLM architectures
- you want a production-ready library like Hugging Face Transformers for training or inference at scale
- you need active maintenance or support for recent PyTorch versions

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, nlp
- domain: deep-learning, tutorials
- platform: python
- tags: transformer, pytorch, attention, attention-is-all-you-need, educational, machine-translation, positional-encodings, jupyter-notebook, natural-language-processing

## Member repositories
- gordicaleksa/pytorch-original-transformer (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:37.616678+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 2026-08-30T06:43:23.569497+00:00, confidence not recorded.
  - readme: https://github.com/gordicaleksa/pytorch-original-transformer (fetched 2026-08-28T04:03:37.616678+00:00, sha 3d53bc1714d6)
  - homepage: https://youtube.com/c/TheAIEpiphany (fetched 2026-08-29T12:46:59.774534+00:00, sha a49038ff34c1)
- Data as of 2026-08-30T08:39:29.467469+00:00.