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---
language:
- pt
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-bnb-4bit
datasets:
- cnmoro/GPT4-500k-Augmented-PTBR-Clean
widget:
- text: Me conte a história do Boto
---

# Boto 7B

<img src="https://i.imgur.com/xscFgqH.png" alt="logo do boto cor-de-rosa" width="300px" />

Boto é um fine-tuning do Mistral 7B para língua portuguesa. O Boto é bem "falante", as respostas tendem a ser longas e nem sempre objetivas por padrão.

Acesse a [demonstração online](https://huggingface.co/spaces/lucianosb/boto-7B) disponível. E cante junto:

[![Foi Boto Sinhá](https://markdown-videos-api.jorgenkh.no/url?url=https%3A%2F%2Fyoutu.be%2FxSyuWFvI9_8%3Fsi%3DSzIMawwQ6sF_xhZK)](https://youtu.be/xSyuWFvI9_8?si=SzIMawwQ6sF_xhZK)

Boto é um nome dado a vários tipos de golfinhos e botos nativos do Amazonas e dos afluentes do rio Orinoco. Alguns botos existem exclusivamente em água doce, e estes são frequentemente considerados golfinhos primitivos.

O “boto” das regiões do rio Amazonas no norte do Brasil é descrito de acordo com o folclore local como assumindo a forma de um humano, também conhecido como Boto cor-de-rosa, e com o hábito de seduzir mulheres humanas e engravidá-las.

Métricas de avaliação em andamento...

## English description

Boto is a fine-tuning of Mistral 7B for portuguese language. Responses tend to be verbose.

Try the [demo](https://huggingface.co/spaces/lucianosb/boto-7B).

Boto is a Portuguese name given to several types of dolphins and river dolphins native to the Amazon and the Orinoco River tributaries. A few botos exist exclusively in fresh water, and these are often considered primitive dolphins.

The "boto" of the Amazon River regions of northern Brazil are described according to local lore as taking the form of a human or merman, also known as Boto cor-de-rosa ("Pink Boto" in Portuguese) and with the habit of seducing human women and impregnating them.

## How to Run on Colab T4

```python
from transformers import AutoTokenizer, pipeline
import torch

model_id = "lucianosb/boto-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.float16,
    device_map="cuda:0"
)

def make_prompt(question):
  return f"""Abaixo está uma instrução que descreve uma tarefa, combinada com uma entrada que fornece contexto adicional.
Escreva uma resposta que complete adequadamente a solicitação.

### Instruction:
{question}

### Response:
"""

question = "Conte a história do boto"

prompt = make_prompt(question)
sequences = pipe(
   prompt,
   do_sample=True,
   num_return_sequences=1,
   eos_token_id=tokenizer.eos_token_id,
   max_length=2048,
   temperature=0.9,
   top_p=0.6,
   repetition_penalty=1.15
)

print(sequences[0]["generated_text"])
```

## Métricas

|  Tasks  |Version|        Filter         |n-shot|Metric|Value |   |Stderr|
|---------|------:|-----------------------|-----:|------|-----:|---|-----:|
|bluex     |    1.1|all                    |     3|acc   |0.0083|±  |0.0020|
|enem      |    1.1|all                    |     3|acc   |0.0014|±  |0.0006|
|oab_exams |    1.5|all                    |     3|acc   |0.0096|±  |0.0012|
|assin2_rte|    1.1|all   |    15|f1_macro|0.9032|±  |0.0042|
|          |       |all   |    15|acc     |0.9032|±  |0.0042|
|assin2_sts|    1.1|all   |    15|pearson |0.4912|±  |0.0141|
|          |       |all   |    15|mse     |1.3185|±  |N/A   |
|faquad_nli|    1.1|all   |    15|f1_macro|0.6104|±  |0.0137|
|          |       |all   |    15|acc     |0.6292|±  |0.0134|
|hatebr_offensive_binary      |      1|all   |    25|f1_macro|0.7888|±  |0.0078|
|                             |       |all   |    25|acc     |0.7936|±  |0.0077|
|portuguese_hate_speech_binary|      1|all   |    25|f1_macro|0.5503|±  |0.0121|
|                             |       |all   |    25|acc     |0.5523|±  |0.0121|


# Uploaded  model

- **Developed by:** lucianosb
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit

This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)