---
tags:
  - translation
  - transformers
  - safetensors
  - chinese
  - vietnamese
  - marian
  - text2text-generation
  - zh-vi
  - chinese-vietnamese
  - marianmt
  - machine-translation
language:
  - zh
  - vi
pipeline_tag: translation
library_name: transformers
license: apache-2.0
---

# MoxhiMT 60 zh-vi

Chinese → Vietnamese Marian-style machine translation model, tuned for
xianxia / web-novel text.

## Intended Use

- Chinese → Vietnamese web novel / fiction translation
- Local or server inference
- Experimental release; review output for high-stakes / publication use

## Model Details

- **Architecture**: Marian seq2seq (8 encoder + 2 decoder layers)
- **Parameters**: ~57M (d_model 576, ffn 2304)
- **Tokenizer**: SentencePiece source/target, joint ZH+VI, vocab 24k
- **Suggested decoding**: `num_beams=4`, `max_length=512`

## Quick Start

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "DanVP/MoxhiMT-60"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

text = "他抬头看向远处的山门。"
inputs = tok(text, return_tensors="pt", truncation=True, max_length=512)
out = model.generate(**inputs, max_length=512, num_beams=4)
print(tok.decode(out[0], skip_special_tokens=True))
```

## CTranslate2 (INT8)

A CTranslate2 INT8 build is included under `ct2-int8/` for faster CPU inference.

```python
import ctranslate2
from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("DanVP/MoxhiMT-60")
translator = ctranslate2.Translator("ct2-int8", device="cpu", compute_type="int8")

text = "他抬头看向远处的山门。"
src = tok.convert_ids_to_tokens(tok(text, truncation=True, max_length=512).input_ids)
results = translator.translate_batch([src], beam_size=4, max_decoding_length=512)
print(tok.decode(tok.convert_tokens_to_ids(results[0].hypotheses[0]), skip_special_tokens=True))
```

## Notes

- Prioritizes translation quality on xianxia / cultivation terminology.
- Trained from scratch with a custom SentencePiece-BPE 24k joint ZH+VI tokenizer.