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---
language:
- pt
- en
license: cc
tags:
- text-generation-inference
- transformers
- mistral
- gguf
- brazil
- brasil
- portuguese
base_model: mistralai/Mistral-7B-Instruct-v0.2
pipeline_tag: text-generation
metrics:
- name: assin2_rte f1_macro
type: assin2_rte
value: 90.13
- name: assin2_rte acc
type: assin2_rte
value: 90.16
- name: assin2_sts pearson
type: assin2_sts
value: 71.51
- name: assin2_sts mse
type: assin2_sts
value: 68.03
- name: bluex acc
type: bluex
value: 47.98
- name: enem acc
type: enem
value: 58.43
- name: faquad_nli f1_macro
type: faquad_nli
value: 64.24
- name: faquad_nli acc
type: faquad_nli
value: 67.69
- name: hatebr_offensive_binary f1_macro
type: hatebr_offensive_binary
value: 83.61
- name: hatebr_offensive_binary acc
type: hatebr_offensive_binary
value: 83.71
- name: oab_exams acc
type: oab_exams
value: 38.41
- name: portuguese_hate_speech_binary f1_macro
type: portuguese_hate_speech_binary
value: 61.87
- name: portuguese_hate_speech_binary acc
type: portuguese_hate_speech_binary
value: 63.22
---
# Cabra Mistral 7b v2
<img src="https://media.discordapp.net/attachments/1060891441724932096/1219303427000242316/blackpantera_cute_goat_with_red_M_in_the_background_brazil_flag_3b448f3a-d500-4f01-877f-2e469aba7dfc.png?ex=660acfce&is=65f85ace&hm=28ee401f092b558b11df54951270189641fe7d1173bfc4a5d633e53fb03c2d6d&=&format=webp&quality=lossless&width=350&height=350" width="400" height="400">
Esse modelo é um finetune do [Mistral 7b Instruct 0.2](https://huggingface.co/mistralai/mistral-7b-instruct-v0.2) com o dataset interno Cabra 10k. Esse modelo é optimizado para português e responde em portuguese nativamente. Ele apresenta melhoria em varios benchmarks brasileiros em comparação com o modelo base.
**Exprimente o nosso demo aqui: [CabraChat](https://huggingface.co/spaces/nicolasdec/CabraChat).**
**Conheça os nossos outros modelos: [Cabra](https://huggingface.co/collections/botbot-ai/models-6604c2069ceef04f834ba99b).**
## Detalhes do Modelo
### Modelo: Mistral 7b Instruct 0.2
Mistral-7B-v0.1 é um modelo de transformador, com as seguintes escolhas arquitetônicas:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
### dataset: Cabra 10k
Dataset interno para finetuning. Vamos lançar em breve.
### Quantização / GGUF
Colocamos diversas versões (GGUF) quantanizadas no branch "quantanization".
### Exemplo
```
<s> [INST] who is Elon Musk? [/INST]Elon Musk é um empreendedor, inventor e capitalista americano. Ele é o fundador, CEO e CTO da SpaceX, CEO da Neuralink e fundador do The Boring Company. Musk também é o proprietário do Twitter.</s>
```
### Paramentros de trainamento
```
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 3
```
### Framework
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.14.6
- Tokenizers 0.15.2
## Uso
O modelo é destinado, por agora, a fins de pesquisa. As áreas e tarefas de pesquisa possíveis incluem:
- Pesquisa sobre modelos gerativos.
- Investigação e compreensão das limitações e viéses de modelos gerativos.
**Proibido para uso comercial. Somente Pesquisa.**
### Evals
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr |
|-----------------------------|---------|----------------------|--------|----------|--------|---------|
| assin2_rte | 1.1 | all | 15 | f1_macro | 0.9013 | ± 0.0043 |
| | | all | 15 | acc | 0.9016 | ± 0.0043 |
| assin2_sts | 1.1 | all | 15 | pearson | 0.7151 | ± 0.0074 |
| | | all | 15 | mse | 0.6803 | ± N/A |
| bluex | 1.1 | all | 3 | acc | 0.4798 | ± 0.0107 |
| | | exam_id__USP_2019 | 3 | acc | 0.375 | ± 0.044 |
| | | exam_id__USP_2021 | 3 | acc | 0.3462 | ± 0.0382 |
| | | exam_id__USP_2020 | 3 | acc | 0.4107 | ± 0.0379 |
| | | exam_id__UNICAMP_2018| 3 | acc | 0.4815 | ± 0.0392 |
| | | exam_id__UNICAMP_2020| 3 | acc | 0.4727 | ± 0.0389 |
| | | exam_id__UNICAMP_2021_1| 3 | acc | 0.413 | ± 0.0418 |
| | | exam_id__UNICAMP_2019| 3 | acc | 0.42 | ± 0.0404 |
| | | exam_id__UNICAMP_2022| 3 | acc | 0.5897 | ± 0.0456 |
| | | exam_id__USP_2022 | 3 | acc | 0.449 | ± 0.041 |
| | | exam_id__USP_2024 | 3 | acc | 0.6341 | ± 0.0434 |
| | | exam_id__UNICAMP_2024| 3 | acc | 0.6 | ± 0.0422 |
| | | exam_id__USP_2023 | 3 | acc | 0.5455 | ± 0.0433 |
| | | exam_id__UNICAMP_2023| 3 | acc | 0.5349 | ± 0.044 |
| | | exam_id__USP_2018 | 3 | acc | 0.4815 | ± 0.0393 |
| | | exam_id__UNICAMP_2021_2| 3 | acc | 0.5098 | ± 0.0403 |
| enem | 1.1 | all | 3 | acc | 0.5843 | ± 0.0075 |
| | | exam_id__2010 | 3 | acc | 0.5726 | ± 0.0264 |
| | | exam_id__2009 | 3 | acc | 0.6 | ± 0.0264 |
| | | exam_id__2014 | 3 | acc | 0.633 | ± 0.0268 |
| | | exam_id__2022 | 3 | acc | 0.6165 | ± 0.0243 |
| | | exam_id__2012 | 3 | acc | 0.569 | ± 0.0265 |
| | | exam_id__2013 | 3 | acc | 0.5833 | ± 0.0274 |
| | | exam_id__2016_2 | 3 | acc | 0.5203 | ± 0.026 |
| | | exam_id__2011 | 3 | acc | 0.6325 | ± 0.0257 |
| | | exam_id__2023 | 3 | acc | 0.5778 | ± 0.0246 |
| | | exam_id__2016 | 3 | acc | 0.595 | ± 0.0258 |
| | | exam_id__2017 | 3 | acc | 0.5517 | ± 0.0267 |
| | | exam_id__2015 | 3 | acc | 0.563 | ± 0.0261 |
| faquad_nli | 1.1 | all | 15 | f1_macro | 0.6424 | ± 0.0138 |
| | | all | 15 | acc | 0.6769 | ± 0.013 |
| hatebr_offensive_binary | 1 | all | 25 | f1_macro | 0.8361 | ± 0.007 |
| | | all | 25 | acc | 0.8371 | ± 0.007 |
| oab_exams | 1.5 | all | 3 | acc | 0.3841 | ± 0.006 |
| | | exam_id__2011-03 | 3 | acc | 0.3636 | ± 0.0279 |
| | | exam_id__2014-14 | 3 | acc | 0.475 | ± 0.0323 |
| | | exam_id__2016-21 | 3 | acc | 0.4125 | ± 0.0318 |
| | | exam_id__2012-06a | 3 | acc | 0.3875 | ± 0.0313 |
| | | exam_id__2014-13 | 3 | acc | 0.325 | ± 0.0303 |
| | | exam_id__2015-16 | 3 | acc | 0.425 | ± 0.032 |
| | | exam_id__2010-02 | 3 | acc | 0.4 | ± 0.0283 |
| | | exam_id__2012-08 | 3 | acc | 0.3875 | ± 0.0314 |
| | | exam_id__2011-05 | 3 | acc | 0.375 | ± 0.0312 |
| | | exam_id__2017-22 | 3 | acc | 0.4 | ± 0.0316 |
| | | exam_id__2018-25 | 3 | acc | 0.4125 | ± 0.0318 |
| | | exam_id__2012-09 | 3 | acc | 0.3636 | ± 0.0317 |
| | | exam_id__2017-24 | 3 | acc | 0.3375 | ± 0.0304 |
| | | exam_id__2016-20a | 3 | acc | 0.3125 | ± 0.0299 |
| | | exam_id__2012-06 | 3 | acc | 0.425 | ± 0.0318 |
| | | exam_id__2013-12 | 3 | acc | 0.4375 | ± 0.0321 |
| | | exam_id__2016-20 | 3 | acc | 0.45 | ± 0.0322 |
| | | exam_id__2013-11 | 3 | acc | 0.4 | ± 0.0316 |
| | | exam_id__2015-17 | 3 | acc | 0.4231 | ± 0.0323 |
| | | exam_id__2015-18 | 3 | acc | 0.4 | ± 0.0316 |
| | | exam_id__2017-23 | 3 | acc | 0.35 | ± 0.0308 |
| | | exam_id__2010-01 | 3 | acc | 0.2471 | ± 0.0271 |
| | | exam_id__2011-04 | 3 | acc | 0.375 | ± 0.0313 |
| | | exam_id__2016-19 | 3 | acc | 0.4103 | ± 0.0321 |
| | | exam_id__2013-10 | 3 | acc | 0.3375 | ± 0.0305 |
| | | exam_id__2012-07 | 3 | acc | 0.3625 | ± 0.031 |
| | | exam_id__2014-15 | 3 | acc | 0.3846 | ± 0.0318 |
| portuguese_hate_speech_binary | 1 | all | 25 | f1_macro | 0.6187 | ± 0.0119 |
| | | all | 25 | acc | 0.6322 | ± 0.0117 |
|