Create README.md
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README.md
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---
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language:
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- en
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- zh
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- id
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- th
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- vi
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- ms
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- lo
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- my
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- jv
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- km
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- su
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- tl
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tags:
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- multilingual
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- sea
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- sailor
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- sft
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- chat
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- instruction
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widget:
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- text: 如何制作烤鱼?
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example_title: Chinese
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- text: How to bake fish?
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example_title: English
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- text: Bagaimana cara memanggang ikan?
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example_title: Malay
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- text: วิธีย่างปลา?
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example_title: Thai
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- text: Bagaimana membuat bakaran ikan?
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example_title: Indonesian
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- text: Làm thế nào để nướng cá?
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example_title: Vietnamese
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license: apache-2.0
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base_model:
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- sail/Sailor2-8B
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---
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<div align="center">
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<img src="sailor2_banner.jpg" width="700"/>
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</div>
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> The logo was generated by MidJourney
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Sailor2 is a community-driven initiative that brings cutting-edge multilingual language models to South-East Asia (SEA).
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Our research highlights a strong demand for models in the **8B and 20B parameter** range for production use, alongside **1B models** for specialized applications,
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such as speculative decoding and research purposes.
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These models, released under the **Apache 2.0 license**, provide enhanced accessibility to advanced language technologies across the region.
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Sailor2 builds upon the foundation of the awesome multilingual model [Qwen 2.5](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e) and
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is continuously pre-trained on **500B tokens** to support **15 languages** better with a unified model.
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These languages include English, Chinese, Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray.
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By addressing the growing demand for diverse, robust, and accessible language models,
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Sailor2 seeks to serve the underserved in SEA areas with open, inclusive, and accessible multilingual LLMs.
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Refer to [Sailor2 Website](https://sailorllm.github.io/) for more training details.
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## Model Summary
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- **Model Collections:** [Base Model & Chat Model](https://huggingface.co/collections/sail/sailor2-language-models-674d7c9e6b4dbbd9a869906b)
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- **Project Website:** [sailorllm.github.io](https://sailorllm.github.io/)
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- **Codebase:** [github.com/sail-sg/sailor2](https://github.com/sail-sg/sailor2)
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- **Technical Report:** Coming Soon
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## Training details
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## Requirements
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The code of Sailor2 has been in the latest Hugging face transformers and we advise you to install `transformers==4.46.3`.
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## Quickstart
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Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda"
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model = AutoModelForCausalLM.from_pretrained(
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'sail/Sailor2-20B-Chat',
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained('sail/Sailor2-8B-Chat')
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system_prompt= \
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'You are an AI assistant named Sailor2, created by Sea AI Lab. \
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As an AI assistant, you can answer questions in English, Chinese, and Southeast Asian languages \
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such as Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray. \
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Your responses should be friendly, unbiased, informative, detailed, and faithful.'
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prompt = "Beri saya pengenalan singkat tentang model bahasa besar."
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# prompt = "Hãy cho tôi một giới thiệu ngắn gọn về mô hình ngôn ngữ lớn."
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# prompt = "ให้ฉันแนะนำสั้น ๆ เกี่ยวกับโมเดลภาษาขนาดใหญ่"
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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input_ids = model_inputs.input_ids.to(device)
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generated_ids = model.generate(
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input_ids,
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max_new_tokens=512,
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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# License
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Sailor2 is distributed under the terms of the Apache License 2.0.
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No restrict on the research and the commercial use.
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## Citation
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If you find Sailor2 useful, please cite our work as follows:
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```
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@misc{sailor2report,
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title={Sailor2: Sailing in South-East Asia with Inclusive Multilingual LLM},
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author={Sailor2 Team},
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year={2024}
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}
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```
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# Contact Us
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If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]).
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