--- 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-Chat ---
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). 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. ### GGUF model list | Name | Quant method | Bits | Size | Use case | | ------------------------------------------------------------ | ------------ | ---- | -------- | -------------------------------------- | | [ggml-model-Q2_K.gguf](https://huggingface.co/sail/Sailor-14B-Chat-gguf/blob/main/ggml-model-Q2_K.gguf) | Q2_K | 2 | 5.91 GB | medium, significant quality loss | | [ggml-model-Q3_K_M.gguf](https://huggingface.co/sail/Sailor-14B-Chat-gguf/blob/main/ggml-model-Q3_K_M.gguf) | Q3_K_M | 3 | 7.42 GB | medium, balanced quality | | [ggml-model-Q3_K_S.gguf](https://huggingface.co/sail/Sailor-14B-Chat-gguf/blob/main/ggml-model-Q3_K_S.gguf) | Q3_K_S | 3 | 6.77 GB | medium, high quality loss | | [ggml-model-Q4_K_M.gguf](https://huggingface.co/sail/Sailor-14B-Chat-gguf/blob/main/ggml-model-Q4_K_M.gguf) | Q4_K_M | 4 | 9.19 GB | large, balanced quality | | [ggml-model-Q4_K_S.gguf](https://huggingface.co/sail/Sailor-14B-Chat-gguf/blob/main/ggml-model-Q4_K_S.gguf) | Q4_K_S | 4 | 8.56 GB | large, greater quality loss | | [ggml-model-Q5_K_M.gguf](https://huggingface.co/sail/Sailor-14B-Chat-gguf/blob/main/ggml-model-Q5_K_M.gguf) | Q5_K_M | 5 | 10.5 GB | large, balanced quality | | [ggml-model-Q5_K_S.gguf](https://huggingface.co/sail/Sailor-14B-Chat-gguf/blob/main/ggml-model-Q5_K_S.gguf) | Q5_K_S | 5 | 10.0 GB | large, very low quality loss | | [ggml-model-Q6_K.gguf](https://huggingface.co/sail/Sailor-14B-Chat-gguf/blob/main/ggml-model-Q6_K.gguf) | Q6_K | 6 | 12.3 GB | large, extremely low quality loss | | [ggml-model-Q8_0.gguf](https://huggingface.co/sail/Sailor-14B-Chat-gguf/blob/main/ggml-model-Q8_0.gguf) | Q8_0 | 8 | 15.1 GB | very large, extremely low quality loss | | [ggml-model-f16.gguf](https://huggingface.co/sail/Sailor-14B-Chat-gguf/blob/main/ggml-model-f16.gguf) | f16 | 16 | 28.3 GB | very large, no quality loss | ### How to run with `llama.cpp` ```shell # install llama.cpp git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp make pip install -r requirements.txt # generate with llama.cpp ./main -ngl 40 -m ggml-model-Q4_K_M.gguf -p "<|im_start|>assistant\nCara memanggang ikan?\n<|im_start|>user\n" --temp 0.7 --repeat_penalty 1.1 -n 400 -e ``` > Change `-ngl 40` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. ### How to run with `llama-cpp-python` ```shell pip install llama-cpp-python ``` ```python import llama_cpp import llama_cpp.llama_tokenizer # load model llama = llama_cpp.Llama.from_pretrained( repo_id="sail/Sailor-14B-Chat-gguf", filename="ggml-model-Q4_K_M.gguf", tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained("sail/Sailor-4B-Chat"), n_gpu_layers=40, n_threads=8, verbose=False, ) system_role= 'system' user_role = 'assistant' assistant_role = "user" 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.' system_prompt = f"<|im_start|>{system_role}\n{system_prompt}<|im_end|>" # inference example output = llama( system_prompt + '\n' + f"<|im_start|>{user_role}\nCara memanggang ikan?\n<|im_start|>{assistant_role}\n", max_tokens=256, temperature=0.7, top_p=0.75, top_k=60, stop=["<|im_end|>", "<|endoftext|>"] ) print(output['choices'][0]['text']) ``` ### How to build demo Install `llama-cpp-python` and `gradio`, then run [script](https://github.com/sail-sg/sailor-llm/blob/main/demo/llamacpp_demo.py). # 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: ``` @article{dou2024sailor, title={Sailor: Open Language Models for South-East Asia}, author={Dou, Longxu and Liu, Qian and Zeng, Guangtao and Guo, Jia and Zhou, Jiahui and Lu, Wei and Lin, Min}, journal={arXiv preprint arXiv:2404.03608}, year={2024} } ``` # 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).