Mxode / NanoTranslator-365M-immersive_translate-v1

NanoTranslator-365M-immersive_translate-v1

English | 简体中文

Introduction

NanoTranslator-365M-immersive_translate-v1 is a translation model specifically designed for Chinese-English bilingual translation, trained with 200M data from the wmt-19 dataset, based on NanoLM-365M-Base .

This model is trained following the Immersive Translate prompt format and can be deployed as an OpenAI format interface using tools like vllm and lmdeploy for utilization.

How to use

Below is a method to call the model using transformers. The prompt follows the immersive translation format to ensure optimal results.

import torch
from typing import Literal
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = 'Mxode/NanoTranslator-365M-immersive_translate-v1'

model = AutoModelForCausalLM.from_pretrained(model_path).to('cuda:0', torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_path)

def translate(
    text: str,
    to: Literal["chinese", "english"] = "chinese",
    **kwargs
):
    generation_args = dict(
        max_new_tokens = kwargs.pop("max_new_tokens", 512),
        do_sample = kwargs.pop("do_sample", True),
        temperature = kwargs.pop("temperature", 0.35),
        top_p = kwargs.pop("top_p", 0.8),
        top_k = kwargs.pop("top_k", 40),
        **kwargs
    )

    prompt = """Translate the following source text to {to}. Output translation directly without any additional text.
    Source Text: {text}

    Translated Text:"""

    messages = [
        {"role": "system", "content": "You are a professional, authentic machine translation engine."},
        {"role": "user", "content": prompt.format(to=to, text=text)}
    ]
    inputs = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    model_inputs = tokenizer([inputs], return_tensors="pt").to(model.device)

    generated_ids = model.generate(model_inputs.input_ids, **generation_args)
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]

    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return response

text = "After a long day at work, I love to unwind by cooking a nice dinner and watching my favorite TV series. It really helps me relax and recharge for the next day."
response = translate(text=text, to='chinese')
print(f'Translation: {response}')

"""
Translation: 工作了一天，我喜欢吃一顿美味的晚餐，看我最喜欢的电视剧，这样做有助于我放松，补充能量。
"""