Quantization made by Richard Erkhov.

sea-lion-7b-instruct - GGUF

Model creator: https://huggingface.co/aisingapore/
Original model: https://huggingface.co/aisingapore/sea-lion-7b-instruct/

Name	Quant method	Size
sea-lion-7b-instruct.Q2_K.gguf	Q2_K	3.05GB
sea-lion-7b-instruct.IQ3_XS.gguf	IQ3_XS	3.34GB
sea-lion-7b-instruct.IQ3_S.gguf	IQ3_S	3.41GB
sea-lion-7b-instruct.Q3_K_S.gguf	Q3_K_S	3.41GB
sea-lion-7b-instruct.IQ3_M.gguf	IQ3_M	3.71GB
sea-lion-7b-instruct.Q3_K.gguf	Q3_K	3.96GB
sea-lion-7b-instruct.Q3_K_M.gguf	Q3_K_M	3.96GB
sea-lion-7b-instruct.Q3_K_L.gguf	Q3_K_L	4.25GB
sea-lion-7b-instruct.IQ4_XS.gguf	IQ4_XS	4.06GB
sea-lion-7b-instruct.Q4_0.gguf	Q4_0	4.21GB
sea-lion-7b-instruct.IQ4_NL.gguf	IQ4_NL	4.24GB
sea-lion-7b-instruct.Q4_K_S.gguf	Q4_K_S	4.24GB
sea-lion-7b-instruct.Q4_K.gguf	Q4_K	4.65GB
sea-lion-7b-instruct.Q4_K_M.gguf	Q4_K_M	4.65GB
sea-lion-7b-instruct.Q4_1.gguf	Q4_1	4.58GB
sea-lion-7b-instruct.Q5_0.gguf	Q5_0	4.96GB
sea-lion-7b-instruct.Q5_K_S.gguf	Q5_K_S	4.96GB
sea-lion-7b-instruct.Q5_K.gguf	Q5_K	5.29GB
sea-lion-7b-instruct.Q5_K_M.gguf	Q5_K_M	5.29GB
sea-lion-7b-instruct.Q5_1.gguf	Q5_1	5.33GB
sea-lion-7b-instruct.Q6_K.gguf	Q6_K	5.75GB

Original model description:

license: mit language: - en - zh - id - ms - tl - my - vi - th - lo - km - ta

SEA-LION-7B-Instruct

SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region. The sizes of the models range from 3 billion to 7 billion parameters.

SEA-LION-7B-Instruct is a multilingual model which has been fine-tuned with thousands of English and Indonesian instruction-completion pairs alongside a smaller pool of instruction-completion pairs from other ASEAN languages. These instructions have been carefully curated and rewritten to ensure the model was trained on truly open, commercially permissive and high quality datasets.

SEA-LION stands for Southeast Asian Languages In One Network.

Developed by: Products Pillar, AI Singapore
Funded by: Singapore NRF
Model type: Decoder
Languages: English, Chinese, Indonesian, Malay, Thai, Vietnamese, Filipino, Tamil, Burmese, Khmer, Lao
License: MIT License

Model Details

Base model

We performed instruction tuning in English and Indonesian on our pre-trained SEA-LION-7B, a decoder model using the MPT architecture, to create SEA-LION-7B-Instruct.

Benchmark Performance

We evaluated SEA-LION-7B-Instruct on the BHASA benchmark (arXiv and GitHub) across a variety of tasks.

BHASA stands out amongst other evaluations for SEA languages for its holistic approach to evaluation, including not just traditional Natural Language Processing (NLP) benchmarking tasks (such as sentiment analysis and question answering), but also linguistic and cultural diagnostic tests which are meticulously handcrafted.

The evaluation was done zero-shot with Indonesian prompts and only a sample of 100-1000 instances for each dataset was used as per the setting described in the BHASA paper. The scores shown in the table below have been adjusted to only consider answers provided in the appropriate language.

Model	QA (F1)	Sentiment (F1)	Toxicity (F1)	Eng>Indo (ChrF++)	Indo>Eng (ChrF++)	Summary (ROUGE-L)	NLI (Acc)	Causal (Acc)
SEA-LION-7B-Instruct-Research	24.86	76.13	24.45	52.50	46.82	15.44	33.20	23.80
SEA-LION-7B-Instruct	68.41	91.45	17.98	57.48	58.04	17.54	53.10	60.80
SeaLLM 7B v1	30.96	56.29	22.60	62.23	41.55	14.03	26.50	56.60
SeaLLM 7B v2	44.40	80.13	55.24	64.01	63.28	17.31	43.60	82.00
Sailor-7B (Base)	65.43	59.48	20.48	64.27	60.68	8.69	15.10	38.40
Sailor-7B-Chat	38.02	87.64	52.07	64.25	61.87	15.28	68.30	85.60
Llama 2 7B Chat	11.12	52.32	0.00	44.09	57.58	9.24	0.00	0.00
Mistral 7B Instruct v0.1	38.85	74.38	20.83	30.60	51.43	15.63	28.60	50.80
GPT-4 (gpt-4-0314)	73.60	74.14	63.96	69.38	67.53	18.71	83.20	96.00

For Natural Language Understanding (NLU) tasks, we tested the model on Sentiment Analysis (Sentiment) using the NusaX dataset, Question Answering (QA) using the TyDiQA dataset, and Toxicity Detection (Toxicity) using the Indonesian Multi-Label Hate Speech Detection dataset. The metrics used are F1 scores for all three tasks.
For Natural Language Generation (NLG) tasks, we tested the model on Machine Translation from English to Indonesian (Eng>Indo) and from Indonesian to English (Indo>Eng) using the FLORES-200 dataset, and Abstractive Summarization (Summary) using the XLSum dataset. The metrics used for Machine Translation and Abstractive Summarization are ChrF++ and ROUGE-L respectively.
For Natural Language Reasoning (NLR) tasks, we tested the model on Natural Language Inference (NLI) using the IndoNLI lay dataset and on Causal Reasoning (Causal) using the XCOPA dataset. The metrics are based on accuracy for both tasks.

Usage

SEA-LION can be run using the 🤗 Transformers library

# Please use transformers==4.37.2

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("aisingapore/sea-lion-7b-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("aisingapore/sea-lion-7b-instruct", trust_remote_code=True)

prompt_template = "### USER:\n{human_prompt}\n\n### RESPONSE:\n"
prompt = """Apa sentimen dari kalimat berikut ini?
Kalimat: Buku ini sangat membosankan.
Jawaban: """
full_prompt = prompt_template.format(human_prompt=prompt)

tokens = tokenizer(full_prompt, return_tensors="pt")
output = model.generate(tokens["input_ids"], max_new_tokens=20, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Prompting Guide

Coming soon

Caveats

It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Firstly, like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning. Finally, it should be noted that the model has not been optimized for multi-turn dialogue interactions, which may result in reduced effectiveness in extended conversations.

Limitations

Safety

Current SEA-LION models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.

Commercially Non-Permissive and Commercially Permissive SEA-LION Releases

The previous release of the commercially non-permissive SEA-LION-Instruct-Research enabled us to explore the full research potential of SEA-LION when allowed to take full advantage of what is publicly available. In contrast, in building the commercially permissive SEA-LION-7B-Instruct, we had to leave out high-quality instruction data that was either proprietary, restricted by non-commercial licenses or in a legal gray area, leaving us with a much smaller proportion of commercially permissive data to work with — a problem that is even more pronounced for low-resource languages. We thus hope this will sound a call to action for more initiatives to create commercially viable data in the region, enabling practical benefits for all.

Technical Specifications

Fine-Tuning Details

The SEA-LION-7B-Instruct was fine-tuned using 8x A100-40GB using parameter efficient fine tuning in the form of LoRA.

Data

SEA-LION-7B-Instruct was trained on a wide range of instructions that were manually and stringently verified by our team. A large portion of the effort was dedicated to ensuring that each instruction-completion pair that the model sees is of a high quality and any errors were corrected and rewritten by native speakers or else dropped from our mix.

In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source.

Link to dataset: coming soon

Call for Contributions

We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions.

The Team

Lau Wayne
Leong Wei Qi
Li Yier
Liu Bing Jie Darius
Lovenia Holy
Montalan Jann Railey
Ng Boon Cheong Raymond
Ngui Jian Gang
Nguyen Thanh Ngan
Ong Tat-Wee David
Rengarajan Hamsawardhini
Siow Bryan
Susanto Yosephine
Tai Ngee Chia
Tan Choon Meng
Teng Walter
Teo Eng Sipp Leslie
Teo Wei Yi
Tjhi William
Yeo Yeow Tong
Yong Xianbin

Acknowledgements

AI Singapore is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.

Contact

For more info, please contact us using this SEA-LION Inquiry Form

Link to SEA-LION's GitHub repository

Disclaimer

This is the repository for the commercial instruction-tuned model. The model has not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.