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RichardErkhov/
sail_-_Sailor-7B-gguf

Name: sail_-_Sailor-7B-gguf
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Sailor-7B - GGUF

Model creator: https://huggingface.co/sail/
Original model: https://huggingface.co/sail/Sailor-7B/

Name	Quant method	Size
Sailor-7B.Q2_K.gguf	Q2_K	2.89GB
Sailor-7B.IQ3_XS.gguf	IQ3_XS	3.18GB
Sailor-7B.IQ3_S.gguf	IQ3_S	3.32GB
Sailor-7B.Q3_K_S.gguf	Q3_K_S	3.32GB
Sailor-7B.IQ3_M.gguf	IQ3_M	3.48GB
Sailor-7B.Q3_K.gguf	Q3_K	3.65GB
Sailor-7B.Q3_K_M.gguf	Q3_K_M	3.65GB
Sailor-7B.Q3_K_L.gguf	Q3_K_L	3.93GB
Sailor-7B.IQ4_XS.gguf	IQ4_XS	4.02GB
Sailor-7B.Q4_0.gguf	Q4_0	4.2GB
Sailor-7B.IQ4_NL.gguf	IQ4_NL	4.22GB
Sailor-7B.Q4_K_S.gguf	Q4_K_S	4.23GB
Sailor-7B.Q4_K.gguf	Q4_K	4.44GB
Sailor-7B.Q4_K_M.gguf	Q4_K_M	4.44GB
Sailor-7B.Q4_1.gguf	Q4_1	4.62GB
Sailor-7B.Q5_0.gguf	Q5_0	5.03GB
Sailor-7B.Q5_K_S.gguf	Q5_K_S	5.03GB
Sailor-7B.Q5_K.gguf	Q5_K	5.15GB
Sailor-7B.Q5_K_M.gguf	Q5_K_M	5.15GB
Sailor-7B.Q5_1.gguf	Q5_1	5.44GB
Sailor-7B.Q6_K.gguf	Q6_K	5.91GB
Sailor-7B.Q8_0.gguf	Q8_0	7.65GB

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/Qwen1.5-7B model-index:
name: Sailor-7B results:
- task: type: text-generation dataset: name: XQuAD-Thai type: XQuAD-Thai metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 57.88
  - name: F1 (3-Shot) type: F1 (3-Shot) value: 71.06
- task: type: text-generation dataset: name: TyDiQA-Indonesian type: TyDiQA-Indonesian metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 60.53
  - name: F1 (3-Shot) type: F1 (3-Shot) value: 75.42
- task: type: text-generation dataset: name: XQuAD-Vietnamese type: XQuAD-Vietnamese metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 53.81
  - name: F1 (3-Shot) type: F1 (3-Shot) value: 74.62
- task: type: text-generation dataset: name: XCOPA-Thai type: XCOPA-Thai metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 59.00
- task: type: text-generation dataset: name: XCOPA-Indonesian type: XCOPA-Indonesian metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 72.20
- task: type: text-generation dataset: name: XCOPA-Vietnamese type: XCOPA-Vietnamese metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 72.20
- task: type: text-generation dataset: name: M3Exam-Thai type: M3Exam-Thai metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 30.00
- task: type: text-generation dataset: name: M3Exam-Indonesian type: M3Exam-Indonesian metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 32.88
- task: type: text-generation dataset: name: M3Exam-Vietnamese type: M3Exam-Vietnamese metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 44.10
- task: type: text-generation dataset: name: BELEBELE-Thai type: BELEBELE-Thai metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 41.56
- task: type: text-generation dataset: name: BELEBELE-Indonesian type: BELEBELE-Indonesian metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 44.33
- task: type: text-generation dataset: name: BELEBELE-Vietnamese type: BELEBELE-Vietnamese metrics:
  - name: EM (3-Shot) type: EM (3-Shot) value: 45.33

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 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
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.

python

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

model = AutoModelForCausalLM.from_pretrained("sail/Sailor-7B", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("sail/Sailor-7B")

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 you should comply with the Qwen License, which means that you shall request a license from Qwen team if your product or service has more than 100 million monthly active users in your commerical scenarios, otherwise no need for further request.

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 or liuqian@sea.com.

RichardErkhov/sail_-_Sailor-7B-gguf