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sail / Sailor2-8B
Model's Last Updated: February 20 2025
text-generation
Introduction of Sailor2-8B
Model Details of Sailor2-8B
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: sailorllm.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 [email protected] or [email protected] .
Runs of sail Sailor2-8B on huggingface.co
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