18 Mar, 2026
1 commit
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Implemented CTranslate2 for the three local translation models and switched the existing local_nllb / local_marian factories over to it. The new runtime lives in local_ctranslate2.py, including HF->CT2 auto-conversion, float16 compute type mapping, Marian direction handling, and NLLB target-prefix decoding. The service wiring is in service.py (line 113), and the three model configs now point at explicit ctranslate2-float16 dirs in config.yaml (line 133). I also updated the setup path so this is usable end-to-end: ctranslate2>=4.7.0 was added to requirements_translator_service.txt and requirements.txt, the download script now supports pre-conversion in download_translation_models.py (line 27), and the docs/config examples were refreshed in translation/README.md. I installed ctranslate2 into .venv-translator, pre-converted all three models, and the CT2 artifacts are now already on disk: models/translation/facebook/nllb-200-distilled-600M/ctranslate2-float16 models/translation/Helsinki-NLP/opus-mt-zh-en/ctranslate2-float16 models/translation/Helsinki-NLP/opus-mt-en-zh/ctranslate2-float16 Verification was solid. python3 -m compileall passed, direct TranslationService smoke tests ran successfully in .venv-translator, and the focused NLLB benchmark on the local GPU showed a clear win: batch_size=16: HF 0.347s/batch, 46.1 items/s vs CT2 0.130s/batch, 123.0 items/s batch_size=1: HF 0.396s/request vs CT2 0.126s/request One caveat: translation quality on some very short phrases, especially opus-mt-en-zh, still looks a bit rough in smoke tests, so I’d run your real quality set before fully cutting over. If you want, I can take the next step and update the benchmark script/report so you have a fresh full CT2 performance report for all three models.
17 Mar, 2026
1 commit