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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-0.5B |
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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 7B 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:** Coming Soon |
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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 public 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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## Requirements |
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The code of Sailor has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`. |
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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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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda" # the device to load the model |
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model = AutoModelForCausalLM.from_pretrained("sail/Sailor-7B", device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained("sail/Sailor-7B") |
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input_message = "Model bahasa adalah model probabilistik" |
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### The given Indonesian input translates to 'A language model is a probabilistic model of.' |
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model_inputs = tokenizer([input_message], return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=64 |
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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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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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# 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]). |
