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  ---
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  language:
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  - pt
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  language:
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  - pt
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+ ---
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+
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+ Sabiá-7B is Portuguese language model developed by [Maritaca AI](https://www.maritaca.ai/).
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+ **Input:** The model accepts only text input.
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+ **Output:** The Model generates text only.
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+ **Model Architecture:** Sabiá-7B 2 is an auto-regressive language model that uses the same architecture of LLaMA-1-7B.
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+ **Tokenizer:** It uses the same tokenizer as LLaMA-1-7B.
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+ **Maximum sequence length:** 2048 tokens.
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+ **Pretraining data:** The model was pretrained on 7 billion tokens from the Portuguese subset of ClueWeb22, starting with the weights of LLaMA-1-7B and further trained for an additional 10 billion tokens, approximately 1.4 epochs of the training dataset.
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+ **Data Freshness:** The pretraining data has a cutoff of mid-2022.
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+ **License:** The licensing is the same as LLaMA-1's, restricting the model's use to research purposes only.
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+ **Paper:** For more details, please refer to our paper: [Sabiá: Portuguese Large Language Models](https://arxiv.org/pdf/2304.07880.pdf)
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+ Given that Sabiá-7B was trained solely on a language modeling objective without fine-tuning for instruction following, it is recommended for few-shot tasks rather than zero-shot tasks.
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+ **Results**
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+ Below we show the results on the Poeta benchmark, which consists of 14 Portuguese datasets:
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+
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+ |Model | NPM |
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+ |--|--|
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+ |LLaMA-1-7B| 33.0|
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+ |LLaMA-2-7B| 43.7|
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+ |Sabiá-7B| 48.5|
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+ For more information on the Normalized Preferred Metric (NPM), please check out our paper.
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+ Please use the following bibtex to cite our paper:
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+ ```
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+ @InProceedings{10.1007/978-3-031-45392-2_15,
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+ author="Pires, Ramon
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+ and Abonizio, Hugo
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+ and Almeida, Thales Sales
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+ and Nogueira, Rodrigo",
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+ editor="Naldi, Murilo C.
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+ and Bianchi, Reinaldo A. C.",
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+ title="Sabi{\'a}: Portuguese Large Language Models",
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+ booktitle="Intelligent Systems",
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+ year="2023",
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+ publisher="Springer Nature Switzerland",
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+ address="Cham",
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+ pages="226--240",
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+ isbn="978-3-031-45392-2"
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+ }
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+ ```