Create README.md

README.md

@@ -0,0 +1,148 @@
+---
+language:
+- en
+- zh
+- id
+- th
+- vi
+- ms
+- lo
+datasets:
+- CohereForAI/aya_dataset
+- CohereForAI/aya_collection
+- Open-Orca/OpenOrca
+- HuggingFaceH4/ultrachat_200k
+- openbmb/UltraFeedback
+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/Sailor-14B
+---
+
+<div align="center">
+  <img src="banner_sailor.jpg" width="700"/>
+</div>
+
+Sailor is a suite of Open Language Models tailored for South-East Asia (SEA), focusing on languages such as 🇮🇩Indonesian, 🇹🇭Thai, 🇻🇳Vietnamese, 🇲🇾Malay, and 🇱🇦Lao. 
+Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of SEA region. 
+Built from [Qwen 1.5](https://huggingface.co/collections/Qwen/qwen15-65c0a2f577b1ecb76d786524) , Sailor encompasses models of varying sizes, spanning from 0.5B to 14B versions for different requirements. 
+We further fine-tune the base model with open-source datasets to get instruction-tuned models, namedly Sailor-Chat. 
+Benchmarking results demonstrate Sailor's proficiency in tasks such as question answering, commonsense reasoning, and other tasks in SEA languages.
+
+> The logo was generated by MidJourney
+
+## Model Summary
+- **Model Collections:** [Base Model & Chat Model](https://huggingface.co/collections/sail/sailor-65e19a749f978976f1959825)
+- **Project Website:** [sailorllm.github.io](https://sailorllm.github.io/)
+- **Codebase:** [github.com/sail-sg/sailor-llm](https://github.com/sail-sg/sailor-llm)
+- **Technical Report:** [arxiv.org/pdf/2404.03608.pdf](https://arxiv.org/pdf/2404.03608.pdf) 
+
+
+## Training details
+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](https://huggingface.co/datasets/cerebras/SlimPajama-627B), 
+[SkyPile](https://huggingface.co/datasets/Skywork/SkyPile-150B), 
+[CC100](https://huggingface.co/datasets/cc100) and [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400).
+The instruction tuning corpus are all publicly available including 
+[aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection), 
+[aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset), 
+[OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca),
+[UltraChat](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k),
+[UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback).
+
+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.
+
+## Requirements
+The code of Sailor has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`.
+
+## Quickstart
+
+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, AutoTokenizer
+device = "cuda"
+
+model = AutoModelForCausalLM.from_pretrained(
+    'sail/Sailor-7B-Chat',
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+tokenizer = AutoTokenizer.from_pretrained('sail/Sailor-14B-Chat')
+system_prompt= 'You are an AI assistant named Sailor created by Sea AI Lab. As an AI assistant, you need to answer a series of questions next, which may include languages such as English, Chinese, Thai, Vietnamese, Indonesian, Malay, and so on. Your answer should be friendly, unbiased, faithful, informative and detailed.'
+
+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": "assistant", "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
+
+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](https://huggingface.co/Qwen/Qwen1.5-1.8B/blob/main/LICENSE).
+
+## Citation
+
+If you find sailor useful, please cite our work as follows:
+
+```
+@misc{dou2024sailor,
+      title={Sailor: Open Language Models for South-East Asia}, 
+      author={Longxu Dou and Qian Liu and Guangtao Zeng and Jia Guo and Jiahui Zhou and Wei Lu and Min Lin},
+      year={2024},
+      eprint={2404.03608},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+# Contact Us
+
+If you have any questions, please raise an issue or contact us at [doulx@sea.com](mailto:doulx@sea.com) or [liuqian@sea.com](mailto:liuqian@sea.com).