Quantization made by Richard Erkhov.

Sailor-1.8B - GGUF

Model creator: https://huggingface.co/sail/
Original model: https://huggingface.co/sail/Sailor-1.8B/

Name	Quant method	Size
Sailor-1.8B.Q2_K.gguf	Q2_K	0.79GB
Sailor-1.8B.IQ3_XS.gguf	IQ3_XS	0.86GB
Sailor-1.8B.IQ3_S.gguf	IQ3_S	0.89GB
Sailor-1.8B.Q3_K_S.gguf	Q3_K_S	0.89GB
Sailor-1.8B.IQ3_M.gguf	IQ3_M	0.92GB
Sailor-1.8B.Q3_K.gguf	Q3_K	0.95GB
Sailor-1.8B.Q3_K_M.gguf	Q3_K_M	0.95GB
Sailor-1.8B.Q3_K_L.gguf	Q3_K_L	0.98GB
Sailor-1.8B.IQ4_XS.gguf	IQ4_XS	1.01GB
Sailor-1.8B.Q4_0.gguf	Q4_0	1.04GB
Sailor-1.8B.IQ4_NL.gguf	IQ4_NL	1.05GB
Sailor-1.8B.Q4_K_S.gguf	Q4_K_S	1.08GB
Sailor-1.8B.Q4_K.gguf	Q4_K	1.13GB
Sailor-1.8B.Q4_K_M.gguf	Q4_K_M	1.13GB
Sailor-1.8B.Q4_1.gguf	Q4_1	1.13GB
Sailor-1.8B.Q5_0.gguf	Q5_0	1.22GB
Sailor-1.8B.Q5_K_S.gguf	Q5_K_S	1.24GB
Sailor-1.8B.Q5_K.gguf	Q5_K	1.28GB
Sailor-1.8B.Q5_K_M.gguf	Q5_K_M	1.28GB
Sailor-1.8B.Q5_1.gguf	Q5_1	1.31GB
Sailor-1.8B.Q6_K.gguf	Q6_K	1.47GB
Sailor-1.8B.Q8_0.gguf	Q8_0	1.82GB

Original model description:

language: - en - zh - id - th - vi - ms - lo datasets: - cerebras/SlimPajama-627B - Skywork/SkyPile-150B - allenai/MADLAD-400 - cc100 tags: - multilingual - sea - sailor license: apache-2.0 base_model: Qwen/Qwen1.5-1.8B inference: false model-index: - name: Sailor-1.8B results: - task: type: text-generation dataset: name: XQuAD-Thai type: XQuAD-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 32.72 - name: F1 (3-Shot) type: F1 (3-Shot) value: 48.66 - task: type: text-generation dataset: name: TyDiQA-Indonesian type: TyDiQA-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 40.88 - name: F1 (3-Shot) type: F1 (3-Shot) value: 65.37 - task: type: text-generation dataset: name: XQuAD-Vietnamese type: XQuAD-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.22 - name: F1 (3-Shot) type: F1 (3-Shot) value: 53.35 - task: type: text-generation dataset: name: XCOPA-Thai type: XCOPA-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 53.8 - task: type: text-generation dataset: name: XCOPA-Indonesian type: XCOPA-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 64.20 - task: type: text-generation dataset: name: XCOPA-Vietnamese type: XCOPA-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 63.20 - task: type: text-generation dataset: name: M3Exam-Thai type: M3Exam-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 25.38 - task: type: text-generation dataset: name: M3Exam-Indonesian type: M3Exam-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 28.30 - task: type: text-generation dataset: name: M3Exam-Vietnamese type: M3Exam-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.71 - task: type: text-generation dataset: name: BELEBELE-Thai type: BELEBELE-Thai metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.22 - task: type: text-generation dataset: name: BELEBELE-Indonesian type: BELEBELE-Indonesian metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 34.89 - task: type: text-generation dataset: name: BELEBELE-Vietnamese type: BELEBELE-Vietnamese metrics: - name: EM (3-Shot) type: EM (3-Shot) value: 35.33

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 , Sailor encompasses models of varying sizes, spanning from 0.5B to 7B 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
Project Website: sailorllm.github.io
Codebase: github.com/sail-sg/sailor-llm
Technical Report: 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, SkyPile, CC100 and MADLAD-400.

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.

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model

model = AutoModelForCausalLM.from_pretrained("sail/Sailor-1.8B", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("sail/Sailor-1.8B")

input_message = "Model bahasa adalah model probabilistik" 
### The given Indonesian input translates to 'A language model is a probabilistic model of.'

model_inputs = tokenizer([input_message], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=64
)

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.

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 [email protected] or [email protected].