language:
- en
- zh
- id
- th
- vi
- ms
- lo
- my
- jv
- km
- su
- tl
tags:
- multilingual
- sea
- sailor
- sft
- chat
- instruction
widget:
- text: 如何制作烤鱼？
example_title: Chinese
- text: How to bake fish?
example_title: English
- text: Bagaimana cara memanggang ikan?
example_title: Malay
- text: วิธีย่างปลา?
example_title: Thai
- text: Bagaimana membuat bakaran ikan?
example_title: Indonesian
- text: Làm thế nào để nướng cá?
example_title: Vietnamese
license: apache-2.0
base_model:
- sail/Sailor2-20B
The logo was generated by MidJourney
Sailor2 is a community-driven initiative that brings cutting-edge multilingual language models to South-East Asia (SEA). Our research highlights a strong demand for models in the 8B and 20B parameter range for production use, alongside 1B models for specialized applications, such as speculative decoding and research purposes. These models, released under the Apache 2.0 license, provide enhanced accessibility to advanced language technologies across the region.
Sailor2 builds upon the foundation of the awesome multilingual model Qwen 2.5 and is continuously pre-trained on 500B tokens to support 15 languages better with a unified model. These languages include English, Chinese, Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray. By addressing the growing demand for diverse, robust, and accessible language models, Sailor2 seeks to serve the underserved in SEA areas with open, inclusive, and accessible multilingual LLMs.
Refer to Sailor2 Website for more training details.
Model Summary
- Model Collections: Base Model & Chat Model
- Project Website: sailorllm.github.io/blog/sailor2
- Codebase: github.com/sail-sg/sailor2
- Technical Report: Coming Soon
Training details
Requirements
The code of Sailor2 has been in the latest Hugging face transformers and we advise you to install transformers==4.46.3.
Quickstart
Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained(
'sail/Sailor2-20B-Chat',
torch_dtype=torch.bfloat16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained('sail/Sailor2-20B-Chat')
system_prompt= \
'You are an AI assistant named Sailor2, created by Sea AI Lab. \
As an AI assistant, you can answer questions in English, Chinese, and Southeast Asian languages \
such as Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray. \
Your responses should be friendly, unbiased, informative, detailed, and faithful.'
prompt = "Beri saya pengenalan singkat tentang model bahasa besar."
# prompt = "Hãy cho tôi một giới thiệu ngắn gọn về mô hình ngôn ngữ lớn."
# prompt = "ให้ฉันแนะนำสั้น ๆ เกี่ยวกับโมเดลภาษาขนาดใหญ่"
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
input_ids = model_inputs.input_ids.to(device)
generated_ids = model.generate(
input_ids,
max_new_tokens=512,
)
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]
print(response)
License
Sailor2 is distributed under the terms of the Apache License 2.0. No restrict on the research and the commercial use.
Citation
If you find Sailor2 useful, please cite our work as follows:
@misc{sailor2report,
title={Sailor2: Sailing in South-East Asia with Inclusive Multilingual LLM},
author={Sailor2 Team},
year={2024}
}
Contact Us
If you have any questions, please raise an issue or contact us at doulx@sea.com or liuqian.sea@gmail.com.