# facebookresearch/fairseq-lua

Facebook AI Research Sequence-to-Sequence Toolkit

Repository: https://github.com/facebookresearch/fairseq-lua
Canonical: https://ross.abutalabs.com/products/fairseq-lua
Language: Lua
License: NOASSERTION
License Family: other
Archived: true
Last push: 2021-09-17T09:21:31+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3461, "days_push": 1811, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3725, forks 604 (observed 2026-08-28T04:08:15.632181+00:00)

## What it is
Fairseq-lua is Facebook AI Research's sequence-to-sequence learning toolkit for the Torch framework, focused on neural machine translation with convolutional and LSTM models. It is the legacy Lua version, now superseded by fairseq-py and provided without support.

## Use cases
- train neural machine translation models
- run convolutional sequence-to-sequence translation
- translate text with pre-trained NMT models
- train seq2seq models on multiple GPUs
- run fast beam search decoding on GPU

## When to choose
- you specifically need the original ConvS2S Lua/Torch implementation for research reproduction
- you maintain a legacy Torch-based NMT pipeline

## When to avoid
- you want an actively maintained toolkit - use fairseq-py instead
- you need Windows support or modern GPU stacks
- you are starting a new machine translation project

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, deep-learning, llm-training, cli
- domain: machine-learning, deep-learning
- platform: lua
- tags: neural-machine-translation, sequence-to-sequence, torch, convolutional-models, beam-search, deprecated, natural-language-processing, linux, macos, gpu

## Member repositories
- facebookresearch/fairseq-lua (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:15.632181+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 2026-08-29T18:29:16.132627+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/fairseq-lua (fetched 2026-08-28T04:08:15.632181+00:00, sha fa3fcb04596b)
- Data as of 2026-08-30T08:39:29.467469+00:00.