# Fast-Inference with Ctranslate2
Speedup inference by 2x-8x using int8 inference in C++
quantized version of Helsinki-NLP/opus-mt-fr-en
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
Converted using
ct2-transformers-converter --model Helsinki-NLP/opus-mt-fr-en --output_dir /home/michael/tmp-ct2fast-opus-mt-fr-en --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
Checkpoint compatible to ctranslate2 and hf-hub-ctranslate2
compute_type=int8_float16
fordevice="cuda"
compute_type=int8
fordevice="cpu"
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-opus-mt-fr-en"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = TranslatorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing?"],
)
print(outputs)
Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
Original description
opus-mt-fr-en
source languages: fr
target languages: en
OPUS readme: fr-en
dataset: opus
model: transformer-align
pre-processing: normalization + SentencePiece
download original weights: opus-2020-02-26.zip
test set translations: opus-2020-02-26.test.txt
test set scores: opus-2020-02-26.eval.txt
Benchmarks
testset | BLEU | chr-F |
---|---|---|
newsdiscussdev2015-enfr.fr.en | 33.1 | 0.580 |
newsdiscusstest2015-enfr.fr.en | 38.7 | 0.614 |
newssyscomb2009.fr.en | 30.3 | 0.569 |
news-test2008.fr.en | 26.2 | 0.542 |
newstest2009.fr.en | 30.2 | 0.570 |
newstest2010.fr.en | 32.2 | 0.590 |
newstest2011.fr.en | 33.0 | 0.597 |
newstest2012.fr.en | 32.8 | 0.591 |
newstest2013.fr.en | 33.9 | 0.591 |
newstest2014-fren.fr.en | 37.8 | 0.633 |
Tatoeba.fr.en | 57.5 | 0.720 |
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