idiap/fast-transformers
Pytorch library for fast transformer implementations observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2270
- days_rel: n/a
- days_push: 1259
- n_releases_24m: 0
Adoption not part of the score
1779 stars · 190 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A PyTorch library providing fast transformer implementations with efficient attention mechanisms such as linear attention, designed to scale transformers to long sequences. It includes CUDA-accelerated kernels and builders for constructing transformer encoders with pluggable attention types.
Use cases
- train transformers on long sequences without quadratic attention cost
- benchmark softmax attention vs linear attention speed
- implement research papers on fast attention mechanisms
- build transformer encoders with custom attention types in PyTorch
- speed up sequence encoding on GPU with CUDA kernels
When to avoid
- you need a maintained library with active releases and support
- you want a general-purpose transformer toolkit like Hugging Face Transformers
- you cannot compile C++/CUDA extensions on your machine
Facets
library · maturity maintenance
machine-learning deep-learning machine-learning deep-learning python windows pytorch transformers attention linear-attention long-sequences cuda natural-language-processing linux macos gpu
1 source
- readme: https://github.com/idiap/fast-transformers · fetched 2026-08-28 · 0d2f37c4cd01
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| idiap/fast-transformers | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="idiap/fast-transformers")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem