KJ-X Korean–Japanese Translation

KJ-X is a custom PyTorch encoder-decoder model for bidirectional Korean↔Japanese translation. The uploaded release contains inference exports and its SentencePiece tokenizer; training datasets and training checkpoints are not distributed.

Files

model_ema.pt: recommended FP32 EMA inference export
model_int8.pt: smaller CPU-only dynamic INT8 export
kjx.model, kjx.vocab, token_features.npz: tokenizer files

The model uses custom code from gaon12/sion_translate and is not a Transformers AutoModel checkpoint.

Quick start

python -m pip install "sion-translate @ git+https://github.com/gaon12/sion_translate.git@ad09f1bb906bf30ae15cbd177df549ac812cc853"
hf download gaon12/sion_translate \
  model_ema.pt kjx.model kjx.vocab token_features.npz \
  --local-dir models/sion_translate

from sion_translate.inference import Translator

translator = Translator(
    "models/sion_translate/model_ema.pt",
    "models/sion_translate/kjx.model",
    device="cuda",  # Use "cpu" when CUDA is unavailable.
)
print(translator.translate(["안녕하세요."], target_language="ja", num_beams=4)[0])
print(translator.translate(["こんにちは。"], target_language="ko", num_beams=4)[0])

Translator automatically finds token_features.npz beside the model/tokenizer. The model is custom code, so transformers.AutoModel and hosted Transformers inference are not supported.

For a smaller CPU download, replace model_ema.pt with model_int8.pt and set device="cpu". The INT8 export is CPU-only. Only load PyTorch artifacts from repositories you trust.

Reproducible smoke test

After downloading the files above, run this from a clean environment to exercise both directions:

python - <<'PY'
from sion_translate.inference import Translator

translator = Translator("models/sion_translate/model_ema.pt", "models/sion_translate/kjx.model")
for text, target in [("안녕하세요.", "ja"), ("こんにちは。", "ko")]:
    output = translator.translate([text], target_language=target, num_beams=1)[0]
    assert isinstance(output, str) and output.strip(), output
    print(f"{text} -> {output}")
PY

Evaluation

The GitHub repository includes a small, independently authored diagnostic JSONL covering both directions and provides a common scorer for KJ-X, LibreTranslate, Papago, Google Translation, DeepL, M2M100 418M, and NLLB-200. No universal quality claim is made from that small set.

Limitations

Machine translations can omit, add, or reverse meaning.
Proper nouns, short context-dependent utterances, specialist terminology, and long inputs need human review.
Do not use unreviewed output for medical, legal, safety-critical, or certified translation.
The custom architecture requires the repository code; hosted Transformers inference is not available out of the box.

License

The original KJ-X code and uploaded model release are provided under the MIT License. This does not grant rights to user-provided inputs, generated service outputs, third-party software, or third-party models used only as comparison baselines.

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