---
language:
- pt
license: apache-2.0
library_name: transformers
tags:
- Misral
- Portuguese
- 7b
base_model: mistralai/Mistral-7B-Instruct-v0.2
datasets:
- pablo-moreira/gpt4all-j-prompt-generations-pt
- rhaymison/superset
pipeline_tag: text-generation
model-index:
- name: Mistral-portuguese-luana-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: 58.64
name: accuracy
source:
url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-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: 47.98
name: accuracy
source:
url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-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: 38.82
name: accuracy
source:
url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-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: 90.63
name: f1-macro
source:
url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-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: 75.81
name: pearson
source:
url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-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: 57.79
name: f1-macro
source:
url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-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: 77.24
name: f1-macro
source:
url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-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: 68.5
name: f1-macro
source:
url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-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: 63.0
name: f1-macro
source:
url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
name: Open Portuguese LLM Leaderboard
---
# Mistral-portuguese-luana-7b
This model was trained with a superset of 200,000 instructions in Portuguese.
The model comes to help fill the gap in models in Portuguese. Tuned from the Mistral 7b, the model was adjusted mainly for instructional tasks.
If you are looking for enhanced compatibility, the Luana model also has a GGUF family that can be run with LlamaCpp.
You can explore the GGUF models starting with the one below:
- [Mistral-portuguese-luana-7b-q8-gguf](https://huggingface.co./rhaymison/Mistral-portuguese-luana-7b-q8-gguf)
- [Mistral-portuguese-luana-7b-q8-gguf](https://huggingface.co./rhaymison/Mistral-portuguese-luana-7b-f16-gguf)
Explore this and other models to find the best fit for your needs!
# How to use
### FULL MODEL : A100
### HALF MODEL: L4
### 8bit or 4bit : T4 or V100
You can use the model in its normal form up to 4-bit quantization. Below we will use both approaches.
Remember that verbs are important in your prompt. Tell your model how to act or behave so that you can guide them along the path of their response.
Important points like these help models (even smaller models like 7b) to perform much better.
```python
!pip install -q -U transformers
!pip install -q -U accelerate
!pip install -q -U bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model = AutoModelForCausalLM.from_pretrained("rhaymison/Mistral-portuguese-luana-7b", device_map= {"": 0})
tokenizer = AutoTokenizer.from_pretrained("rhaymison/Mistral-portuguese-luana-7b")
model.eval()
```
You can use with Pipeline but in this example i will use such as Streaming
```python
inputs = tokenizer([f"""[INST] Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto.
Escreva uma resposta que complete adequadamente o pedido.
### instrução: aja como um professor de matemática e me explique porque 2 + 2 = 4.
[/INST]"""], return_tensors="pt")
inputs.to(model.device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)
```
If you are having a memory problem such as "CUDA Out of memory", you should use 4-bit or 8-bit quantization.
For the complete model in colab you will need the A100.
If you want to use 4bits or 8bits, T4 or L4 will already solve the problem.
# 4bits example
```python
from transformers import BitsAndBytesConfig
import torch
nb_4bit_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True
)
model = AutoModelForCausalLM.from_pretrained(
base_model,
quantization_config=bnb_config,
device_map={"": 0}
)
```
# LangChain
# [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/rhaymison/Mistral-portuguese-luana-7b)
| Metric | Value |
|--------------------------|---------|
|Average |**64.27**|
|ENEM Challenge (No Images)| 58.64|
|BLUEX (No Images) | 47.98|
|OAB Exams | 38.82|
|Assin2 RTE | 90.63|
|Assin2 STS | 75.81|
|FaQuAD NLI | 57.79|
|HateBR Binary | 77.24|
|PT Hate Speech Binary | 68.50|
|tweetSentBR | 63|
### Comments
Any idea, help or report will always be welcome.
email: rhaymisoncristian@gmail.com
