Everything / Models and research / Transformers
Transformers Peaking
On TutorHero's Everything Index, Transformers is peaking on GitHub: 0.73 of every 10,000 new public repos over the last three months (685 a month), +69% vs a year ago. The 12-month forecast says edging up (+18%). By the end of 2032, lines in the same state ended between 0.28x and 4.8x of where they started (median 0.82x).
Since 2019, and the range to 2032
New repos per 10,000 (3-month average, log scale)12-month forecastRange to 2032: middle half and 90% of past similar lines
The range is not a forecast of this line: it is what GitHub lines in the same state (grew its share 15% to 100% in a year) did 1 to 5 years later, 2019 to 2026, applied to today's level. Past 5 years it is stretched, not measured. Checked out of time (learned before 2022, scored after), 71% of outcomes landed inside the 90% range, so read it as wide. Open it in the Everything chart.
Week by week
Wikipedia views a day, last 8 weeks
New public repos with the transformers tag each week (Monday to Sunday, UTC). The outlined bars, last week included, are still filling in, as tags are often added days after a repo is created, so their height will grow a little. "Against the field" divides the change by what the typical tag did, so a wave that lifts every tag does not count. Hover for the numbers.
Built vs talked about
Each line is its own share (of all new GitHub repos, all Hacker News stories, all English Wikipedia views, all new stars on the repos we track), shown as a share of its own peak, so the timing and shape compare even though the units differ. Hover for the counts. Tap a name to hide or show a line.
What it means
- A steady field: Transformers moved +69% in a year, so skills here keep their value without a rush.
- Start by reading rohitg00/ai-engineering-from-scratch (+10.9k stars in 3 months) and MakazhanAlpamys/Soup (+3.5k stars in 3 months).
- People to follow: Yaowei Zheng, Phil Wang, NVIDIA Corporation.
- Getting crowded: new repos +69% but attention -51%, so each new project competes harder for stars.
- 13.5 new stars per new repo over 3 months, vs 8.50 for the typical trend: more demand per project than most fields.
- The 12-month forecast for its share: +18% (Edging up).
- Talk and building in step: Hacker News -47% vs new repos +69% in a year, close to the typical trend.
- History: of past GitHub lines that grew its share 15% to 100% in a year, 49% had a smaller share 3 to 5 years later and 13% at least doubled.
- Attention: 1.28% of new stars on the repos we track, -51% in a year.
- Hacker News: 6 stories a month, -47% share in a year; Wikipedia 38.2k views a month, n/a.
- Not investment advice: this measures what people build on GitHub, not any company.
- Most-starred tools here lately: rohitg00/ai-engineering-from-scratch (vouched), MakazhanAlpamys/Soup (vouched).
- A settled field: favour tools with a long record.
Repos carrying it now
People building it
Yaowei ZhengEfficient fine-tuning frameworks for large language models.Influence 80
Phil WangImplementing AI papers in PyTorch, including transformers and diffusion models.Influence 81
NVIDIA CorporationAI agent security, GPU software, and large-scale transformer training.Influence 93
EleutherAIEleutherAIInfluence 61
Fareed KhanResearching AI and building open-source AI training and inference projects.Influence 61
NielsRoggeML @HuggingFace. Interested in deep learning, NLP.
Contributed 40+ models to HuggingFace TransformersInfluence 79
OpenRLHFOpen-sourced reinforcement learning from human feedback frameworks.Influence 33
NVIDIA Research ProjectsNeural graphics, generative image models, and efficient synthesis research.Influence 83More in Models and research
Questions
Is Transformers growing on GitHub?
Yes. Its share of all new public GitHub repos is +69% vs a year ago and -4% over the last three months (Sep 2026). Phase: Peaking.
How many new Transformers repos are created each month?
About 685 new public repos a month carry the transformers tag (three-month average to Sep 2026), or 0.73 of every 10,000 new repos.
What will Transformers look like in 2032?
Nobody knows, and we say how much. Of past GitHub lines that grew its share 15% to 100% in a year, the middle half had between 0.53x and 1.41x of their share 3 to 5 years later, and 49% ended smaller.
Is Transformers more talked about than built?
Talk and building in step. Over the year its share of Hacker News stories moved -47% while its share of new GitHub repos moved +69%. New repos grew faster than talk for most AI trends this year, so the label compares it with the typical trend: 1.1 times the talk per builder of the median trend.
Not investment advice. Measurements of what people build and star on GitHub, a statistical forecast, base rates and a model's reading of them. Nothing here is about any company's revenue, valuation or stock.