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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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datasets: |
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- cerebras/SlimPajama-627B |
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- Skywork/SkyPile-150B |
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- allenai/MADLAD-400 |
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- cc100 |
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- CohereForAI/aya_dataset |
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- CohereForAI/aya_collection |
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- Open-Orca/OpenOrca |
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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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license: apache-2.0 |
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base_model: sail/Sailor-7B |
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--- |
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<div align="center"> |
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<img src="banner_sailor.jpg" width="700"/> |
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</div> |
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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. |
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Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of SEA region. |
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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. |
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We further fine-tune the base model with open-source datasets to get instruction-tuned models, namedly Sailor-Chat. |
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Benchmarking results demonstrate Sailor's proficiency in tasks such as question answering, commonsense reasoning, and other tasks in SEA languages. |
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> The logo was generated by MidJourney |
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## Model Summary |
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- **Model Collections:** [Base Model & Chat Model](https://huggingface.co/collections/sail/sailor-65e19a749f978976f1959825) |
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- **Project Website:** [sailorllm.github.io](https://sailorllm.github.io/) |
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- **Codebase:** [github.com/sail-sg/sailor-llm](https://github.com/sail-sg/sailor-llm) |
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- **Technical Report:** [arxiv.org/pdf/2404.03608.pdf](https://arxiv.org/pdf/2404.03608.pdf) |
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## Training details |
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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. |
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The pre-training corpus heavily leverages the publicly available corpus, including |
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[SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B), |
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[SkyPile](https://huggingface.co/datasets/Skywork/SkyPile-150B), |
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[CC100](https://huggingface.co/datasets/cc100) and [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400). |
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The instruction tuning corpus are all publicly available including |
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[aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection), |
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[aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset), |
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[OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca). |
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By employing aggressive data deduplication and careful data cleaning on the collected corpus, we have attained a high-quality dataset spanning various languages. |
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Through systematic experiments to determine the weights of different languages, Sailor models undergo training from 200B to 400B tokens, tailored to different model sizes. |
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The approach boosts their performance on SEA languages while maintaining proficiency in English and Chinese without significant compromise. |
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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. |
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### GGUF model list |
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| Name | Quant method | Bits | Size | Use case | |
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| ------------------------------------------------------------ | ------------ | ---- | -------- | -------------------------------------- | |
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| [ggml-model-Q2_K.gguf](https://huggingface.co/sail/Sailor-7B-Chat-gguf/blob/main/ggml-model-Q2_K.gguf) | Q2_K | 2 | 3.10 GB | medium, significant quality loss | |
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| [ggml-model-Q3_K_L.gguf](https://huggingface.co/sail/Sailor-7B-Chat-gguf/blob/main/ggml-model-Q3_K_L.gguf) | Q3_K_L | 3 | 4.22 GB | large, substantial quality loss | |
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| [ggml-model-Q3_K_M.gguf](https://huggingface.co/sail/Sailor-7B-Chat-gguf/blob/main/ggml-model-Q3_K_M.gguf) | Q3_K_M | 3 | 3.92 GB | medium, balanced quality | |
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| [ggml-model-Q3_K_S.gguf](https://huggingface.co/sail/Sailor-7B-Chat-gguf/blob/main/ggml-model-Q3_K_S.gguf) | Q3_K_S | 3 | 3.57 GB | medium, high quality loss | |
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| [ggml-model-Q4_K_M.gguf](https://huggingface.co/sail/Sailor-7B-Chat-gguf/blob/main/ggml-model-Q4_K_M.gguf) | Q4_K_M | 4 | 4.77 GB | large, balanced quality | |
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| [ggml-model-Q4_K_S.gguf](https://huggingface.co/sail/Sailor-7B-Chat-gguf/blob/main/ggml-model-Q4_K_S.gguf) | Q4_K_S | 4 | 4.54 GB | large, greater quality loss | |
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| [ggml-model-Q5_K_M.gguf](https://huggingface.co/sail/Sailor-7B-Chat-gguf/blob/main/ggml-model-Q5_K_M.gguf) | Q5_K_M | 5 | 5.53 GB | large, balanced quality | |
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| [ggml-model-Q5_K_S.gguf](https://huggingface.co/sail/Sailor-7B-Chat-gguf/blob/main/ggml-model-Q5_K_S.gguf) | Q5_K_S | 5 | 5.4 GB | large, very low quality loss | |
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| [ggml-model-Q6_K.gguf](https://huggingface.co/sail/Sailor-7B-Chat-gguf/blob/main/ggml-model-Q6_K.gguf) | Q6_K | 6 | 6.34 GB | large, extremely low quality loss | |
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| [ggml-model-Q8_0.gguf](https://huggingface.co/sail/Sailor-7B-Chat-gguf/blob/main/ggml-model-Q8_0.gguf) | Q8_0 | 8 | 8.21 GB | very large, extremely low quality loss | |
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| [ggml-model-f16.gguf](https://huggingface.co/sail/Sailor-7B-Chat-gguf/blob/main/ggml-model-f16.gguf) | f16 | 16 | 15.40 GB | very large, no quality loss | |
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### How to run with `llama.cpp` |
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```shell |
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# install llama.cpp |
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git clone https://github.com/ggerganov/llama.cpp.git |
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cd llama.cpp |
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make |
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pip install -r requirements.txt |
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# generate with llama.cpp |
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./main -ngl 32 -m ggml-model-Q4_K_M.gguf -p "<|im_start|>question\nCara memanggang ikan?\n<|im_start|>answer\n" --temp 0.7 --repeat_penalty 1.1 -n 400 -e |
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``` |
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> Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. |
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### How to run with `llama-cpp-python` |
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```shell |
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pip install llama-cpp-python |
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``` |
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```python |
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import llama_cpp |
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import llama_cpp.llama_tokenizer |
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# load model |
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llama = llama_cpp.Llama.from_pretrained( |
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repo_id="sail/Sailor-4B-Chat-gguf", |
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filename="ggml-model-Q4_K_M.gguf", |
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tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained("sail/Sailor-4B-Chat"), |
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n_gpu_layers=40, |
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n_threads=8, |
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verbose=False, |
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) |
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system_role= 'system' |
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user_role = 'question' |
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assistant_role = "answer" |
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system_prompt= \ |
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'You are an AI assistant named Sailor created by Sea AI Lab. \ |
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Your answer should be friendly, unbiased, faithful, informative and detailed.' |
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system_prompt = f"<|im_start|>{system_role}\n{system_prompt}<|im_end|>" |
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# inference example |
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output = llama( |
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system_prompt + '\n' + f"<|im_start|>{user_role}\nCara memanggang ikan?\n<|im_start|>{assistant_role}\n", |
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max_tokens=256, |
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temperature=0.7, |
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top_p=0.75, |
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top_k=60, |
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stop=["<|im_end|>", "<|endoftext|>"] |
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) |
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print(output['choices'][0]['text']) |
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``` |
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### How to build demo |
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Install `llama-cpp-python` and `gradio`, then run [script](https://github.com/sail-sg/sailor-llm/blob/main/demo/llamacpp_demo.py). |
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# License |
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Sailor is distributed under the terms of the Apache License 2.0. |
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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). |
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## Citation |
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If you find sailor useful, please cite our work as follows: |
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``` |
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@article{dou2024sailor, |
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title={Sailor: Open Language Models for South-East Asia}, |
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author={Dou, Longxu and Liu, Qian and Zeng, Guangtao and Guo, Jia and Zhou, Jiahui and Lu, Wei and Lin, Min}, |
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journal={arXiv preprint arXiv:2404.03608}, |
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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 [doulx@sea.com](mailto:doulx@sea.com) or [liuqian@sea.com](mailto:liuqian@sea.com). |
