---
language:
- pt
model-index:
- name: sabia-7b
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: ENEM Challenge (No Images)
      type: eduagarcia/enem_challenge
      split: train
      args:
        num_few_shot: 3
    metrics:
    - type: acc
      value: 55.07
      name: accuracy
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BLUEX (No Images)
      type: eduagarcia-temp/BLUEX_without_images
      split: train
      args:
        num_few_shot: 3
    metrics:
    - type: acc
      value: 47.71
      name: accuracy
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: OAB Exams
      type: eduagarcia/oab_exams
      split: train
      args:
        num_few_shot: 3
    metrics:
    - type: acc
      value: 41.41
      name: accuracy
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Assin2 RTE
      type: assin2
      split: test
      args:
        num_few_shot: 15
    metrics:
    - type: f1_macro
      value: 46.68
      name: f1-macro
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Assin2 STS
      type: eduagarcia/portuguese_benchmark
      split: test
      args:
        num_few_shot: 15
    metrics:
    - type: pearson
      value: 1.89
      name: pearson
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: FaQuAD NLI
      type: ruanchaves/faquad-nli
      split: test
      args:
        num_few_shot: 15
    metrics:
    - type: f1_macro
      value: 58.34
      name: f1-macro
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HateBR Binary
      type: ruanchaves/hatebr
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: f1_macro
      value: 61.93
      name: f1-macro
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: PT Hate Speech Binary
      type: hate_speech_portuguese
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: f1_macro
      value: 64.13
      name: f1-macro
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: tweetSentBR
      type: eduagarcia-temp/tweetsentbr
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: f1_macro
      value: 46.64
      name: f1-macro
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=maritaca-ai/sabia-7b
      name: Open Portuguese LLM Leaderboard
---

Sabiá-7B is Portuguese language model developed by [Maritaca AI](https://www.maritaca.ai/).

**Input:** The model accepts only text input.

**Output:** The Model generates text only.

**Model Architecture:** Sabiá-7B is an auto-regressive language model that uses the same architecture of LLaMA-1-7B.

**Tokenizer:** It uses the same tokenizer as LLaMA-1-7B.

**Maximum sequence length:** 2048 tokens.

**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.

**Data Freshness:** The pretraining data has a cutoff of mid-2022.

**License:** The licensing is the same as LLaMA-1's, restricting the model's use to research purposes only.

**Paper:** For more details, please refer to our paper: [Sabiá: Portuguese Large Language Models](https://arxiv.org/pdf/2304.07880.pdf) 


## Few-shot Example

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, like in the example below.

```python
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM

tokenizer = LlamaTokenizer.from_pretrained("maritaca-ai/sabia-7b")
model = LlamaForCausalLM.from_pretrained(
    "maritaca-ai/sabia-7b",
    device_map="auto",  # Automatically loads the model in the GPU, if there is one. Requires pip install acelerate
    low_cpu_mem_usage=True,
    torch_dtype=torch.bfloat16   # If your GPU does not support bfloat16, change to torch.float16
)  

prompt = """Classifique a resenha de filme como "positiva" ou "negativa".

Resenha: Gostei muito do filme, é o melhor do ano!
Classe: positiva

Resenha: O filme deixa muito a desejar.
Classe: negativa

Resenha: Apesar de longo, valeu o ingresso.
Classe:"""

input_ids = tokenizer(prompt, return_tensors="pt")

output = model.generate(
    input_ids["input_ids"].to("cuda"),
    max_length=1024,
    eos_token_id=tokenizer.encode("\n"))  # Stop generation when a "\n" token is dectected

# The output contains the input tokens, so we have to skip them.
output = output[0][len(input_ids["input_ids"][0]):]

print(tokenizer.decode(output, skip_special_tokens=True))
```

If your GPU does not have enough RAM, try using int8 precision.
However, expect some degradation in the model output quality when compared to fp16 or bf16.
```python
model = LlamaForCausalLM.from_pretrained(
    "maritaca-ai/sabia-7b",
    device_map="auto",
    low_cpu_mem_usage=True,
    load_in_8bit=True,  # Requires pip install bitsandbytes
)
```

## Results in Portuguese

Below we show the results on the Poeta benchmark, which consists of 14 Portuguese datasets.

For more information on the Normalized Preferred Metric (NPM), please refer to our paper.

|Model | NPM |
|--|--|
|LLaMA-1-7B| 33.0|
|LLaMA-2-7B| 43.7|
|Sabiá-7B| 48.5|

## Results in English 

Below we show the average results on 6 English datasets: PIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, and OpenBookQA.

|Model | NPM |
|--|--|
|LLaMA-1-7B| 50.1|
|Sabiá-7B| 49.0|


## Citation

Please use the following bibtex to cite our paper: 
```
@InProceedings{10.1007/978-3-031-45392-2_15,
    author="Pires, Ramon
    and Abonizio, Hugo
    and Almeida, Thales Sales
    and Nogueira, Rodrigo",
    editor="Naldi, Murilo C.
    and Bianchi, Reinaldo A. C.",
    title="Sabi{\'a}: Portuguese Large Language Models",
    booktitle="Intelligent Systems",
    year="2023",
    publisher="Springer Nature Switzerland",
    address="Cham",
    pages="226--240",
    isbn="978-3-031-45392-2"
}
```

# [Open Portuguese LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/maritaca-ai/sabia-7b)

|          Metric          |  Value  |
|--------------------------|---------|
|Average                   |**47.09**|
|ENEM Challenge (No Images)|    55.07|
|BLUEX (No Images)         |    47.71|
|OAB Exams                 |    41.41|
|Assin2 RTE                |    46.68|
|Assin2 STS                |     1.89|
|FaQuAD NLI                |    58.34|
|HateBR Binary             |    61.93|
|PT Hate Speech Binary     |    64.13|
|tweetSentBR               |    46.64|