Nanotranslator Immersive Translate 365M

🚀 NanoTranslator-immersive_translate-365M

NanoTranslator-immersive_translate-365M 是一款专门为中英双语翻译设计的模型。它基于 NanoLM-365M-Base，使用来自 wmt-19 数据集的 600 万条数据进行训练，能有效实现中英双语间的准确翻译。

🚀 快速开始

NanoTranslator-immersive_translate-365M 专为中英双语翻译量身打造。它基于 NanoLM-365M-Base 模型，使用 wmt-19 数据集的 600 万条数据进行训练。该模型按照 Immersive Translate 的提示格式进行训练，可借助 vllm 和 lmdeploy 等工具以 OpenAI 格式接口进行部署和使用。

✨ 主要特性

• 双语翻译：专注于中英双语翻译，满足特定语言需求。
• 数据丰富：使用 600 万条来自 wmt-19 数据集的数据进行训练，保证翻译质量。
• 灵活部署：可按照 Immersive Translate 提示格式训练，借助 vllm 和 lmdeploy 等工具以 OpenAI 格式接口部署。

💻 使用示例

基础用法

以下是使用 transformers 调用该模型的方法，提示遵循沉浸式翻译格式以确保最佳效果。

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

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

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。
"""

📄 许可证

本项目采用 GPL-3.0 许可证。