opus-mt-vi-en — CTranslate2 int8
Quantized by @dekthedev.
CTranslate2 int8 conversion of Helsinki-NLP/opus-mt-vi-en. Optimized for CPU inference, especially ARM64 (Cortex-A series).
Model info
| Property | Value |
|---|---|
| Architecture | MarianMT (Transformer encoder-decoder) |
| Parameters | 74M |
| Direction | Vietnamese → English |
| Quantization | CTranslate2 int8 |
| Disk size | ~145 MB |
| BLEU (Tatoeba VI-EN) | 42.8 |
| Speed on ARM64 (est.) | 300–500 tok/s |
Files
| File | Description |
|---|---|
model.bin |
CTranslate2 int8 weights |
config.json |
CTranslate2 model config |
tokenizer_config.json |
MarianTokenizer config |
vocab.json |
Vocabulary |
source.spm |
SentencePiece source model |
target.spm |
SentencePiece target model |
Usage
import ctranslate2
from transformers import MarianTokenizer
from huggingface_hub import snapshot_download
model_dir = snapshot_download("dekthedev/opus-mt-vi-en-ct2-int8")
translator = ctranslate2.Translator(model_dir, device="cpu")
tokenizer = MarianTokenizer.from_pretrained(model_dir)
def translate(text: str) -> str:
encoded = tokenizer([text], return_tensors=None, padding=False)
src_tokens = [tokenizer.convert_ids_to_tokens(ids) for ids in encoded["input_ids"]]
results = translator.translate_batch(src_tokens)
tgt_tokens = results[0].hypotheses[0]
tgt_ids = tokenizer.convert_tokens_to_ids(tgt_tokens)
return tokenizer.decode(tgt_ids, skip_special_tokens=True)
print(translate("Xin chào, bạn có khỏe không?"))
# → Hello, how are you?
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Base model
Helsinki-NLP/opus-mt-vi-en