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sail
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sail
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sail/Sailor-0.5B
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Sailor is crafted by continually pre-training from language models like the remarkable Qwen 1.5 models, which already has a great performance on SEA languages. The pre-training corpus heavily leverages the publicly available corpus, including SlimPajama, SkyPile, CC100 and MADLAD-400. The instruction tuning corpus are all publicly available including aya_collection, aya_dataset, OpenOrca.
By employing aggressive data deduplication and careful data cleaning on the collected corpus, we have attained a high-quality dataset spanning various languages. Through systematic experiments to determine the weights of different languages, Sailor models undergo training from 200B to 400B tokens, tailored to different model sizes. The approach boosts their performance on SEA languages while maintaining proficiency in English and Chinese without significant compromise. Finally, we continually pre-train the Qwen1.5-0.5B model with 400 Billion tokens, and other models with 200 Billion tokens to obtain the Sailor models.
The code of Sailor has been in the latest Hugging face transformers and we advise you to install transformers>=4.37.0.
Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents.
python
from transformers import AutoModelForCausalLM, AutoTokenizerdevice = "cuda"model = AutoModelForCausalLM.from_pretrained('sail/Sailor-0.5B-Chat',torch_dtype="auto",device_map="auto")tokenizer = AutoTokenizer.from_pretrained('sail/Sailor-0.5B-Chat')system_prompt= 'You are a helpful assistant'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": "question", "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)
Sailor is distributed under the terms of the Apache License 2.0. No restrict on the research and the commercial use, but should comply with the Qwen License.
If you find sailor useful, please cite our work as follows:
markdown
@inproceedings{dou-etal-2024-sailor,title = "Sailor: Open Language Models for South-{E}ast {A}sia",author = "Dou, Longxu and Liu, Qian and Zeng, Guangtao and Guo, Jia and Zhou, Jiahui and Mao, Xin and Jin, Ziqi and Lu, Wei and Lin, Min",booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",year = "2024",}
If you have any questions, please raise an issue or contact us at doulx@sea.com or liuqian.sea@gmail.com.