Flan T5 Base Opus En Id Id En

🚀 flan-t5-base-opus-en-id-id-en

該模型專為實現印尼語和英語之間的多模態翻譯而設計，能夠高效準確地完成兩種語言的互譯任務。

🚀 快速開始

此模型可用於印尼語和英語之間的翻譯。以下是使用該模型進行翻譯的示例。

✨ 主要特性

• 專為印尼語和英語的多模態翻譯而設計。
• 支持在CPU、GPU上運行，還支持不同的精度設置。

📦 安裝指南

運行代碼示例前，請確保安裝了必要的庫：

pip install transformers accelerate bitsandbytes datasets tokenizers torch

💻 使用示例

基礎用法

在CPU上運行模型

from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en")
model = T5ForConditionalGeneration.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en")
input_text = "translate English to Indonesia: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

高級用法

在GPU上運行模型

# pip install accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en")
model = T5ForConditionalGeneration.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en", device_map="auto")
input_text = "translate English to Indonesia: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

在GPU上使用不同精度運行模型

FP16

# pip install accelerate
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-ene")
model = T5ForConditionalGeneration.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en", device_map="auto", torch_dtype=torch.float16)
input_text = "translate English to Indonesia: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

INT8

# pip install bitsandbytes accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en")
model = T5ForConditionalGeneration.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en", device_map="auto", load_in_8bit=True)
input_text = "translate English to Indonesia: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

📚 詳細文檔

模型詳情

模型描述

訓練結果

框架版本

• Transformers 4.29.2
• Pytorch 2.0.1
• Datasets 2.13.1
• Tokenizers 0.13.3

📄 許可證

本模型使用Apache 2.0許可證。