metadata

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:
  - Qwen/Qwen2.5-7B

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. The Sailor2 model comes in three sizes, 1B, 8B, and 20B, which are expanded from the Qwen2.5 base models of 0.5B, 7B, and 14B, respectively.

Model Summary

Model Collections: Base Model & Chat Model
Project Website: sea-sailor.github.io/blog/sailor2/
Codebase: github.com/sail-sg/sailor2
Technical Report: Coming Soon

Training details

During development, we employ a range of advanced technologies to ensure top-tier performance and efficiency:

model expansion
optimized data mixing strategies
multi-stage pre-training protocols
advanced multilingual post-training

Please refer to Sailor2 Blog for more 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.

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model

model = AutoModelForCausalLM.from_pretrained("sail/Sailor2-8B", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("sail/Sailor2-8B")

input_message = "Model bahasa adalah model probabilistik" 
### The given Indonesian input translates to 'A language model is a probabilistic model of.'

model_inputs = tokenizer([input_message], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=64
)

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.