Mistral-8x7b-Quantized-portuguese-luanaa

This model was trained with a superset of 300,000 instructions in Portuguese. The model comes to help fill the gap in models in Portuguese. Tuned from the Mistral 8x7b and quantized in 4bit for Portuguese, the model was adjusted mainly for instructional tasks.

How to use

A100 GPU

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.

!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-8x7b-Quantized-portuguese-luana", device_map= {"": 0})
tokenizer = AutoTokenizer.from_pretrained("rhaymison/Mistral-8x7b-Quantized-portuguese-luana")
model.eval()

You can use with Pipeline but in this example i will use such as Streaming


inputs = tokenizer([f"""<s>[INST] Abaixo estรก uma instruรงรฃo que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto.
Escreva uma resposta que complete adequadamente o pedido.
### instruรงรฃo: aja como um professor de matemรกtica 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

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

Extracting a langChain conversation with a drug petition document as context.

  • Human: Bom dia. Me informe quais medicamentos sรฃo solicitados no texto. Informe tambรฉm a posologia e o perรญodo de tratamento.

  • Luana: Olรก! No texto sรฃo solicitados os medicamentos DULOXETINA 30mg,PREGABALINA 100mg ROSUVASTATINA 10mg. A posologia e o perรญodo de tratamento nรฃo sรฃo especificados, pois isso deve ser indicado periodicamente pelo mรฉdico.

  • Human: Perfeito. E qual รฉ o valor da causa informado no documento?

  • Luana: O valor da causa informado no documento รฉ R$ 6.132,00 (seis mil cento e trรชs).

  • Human: Foi mencionado no texto alguma lei?

  • Luana: Sim, a Lei Complementar nยบ 80/94 รฉ mencionada no texto. Ela se refere ร s prerrogativas institucionais da Defensoria Pรบblica, incluindo a intimaรงรฃo pessoal de seus membros, a contagem em dobro dos prazos processuais, a manifestaรงรฃo por cotas nos autos e a dispensa da procuraรงรฃo.

  • Human: Perfeito! Muito obrigado pela ajuda. Por hora รฉ somente isso =]

  • Luana: Foi um prazer ajudar! Se vocรช tiver outras dรบvidas ou necessitar de mais informaรงรตes, nรฃo hesite em perguntar. Tenha um bom dia!

Open Portuguese LLM Leaderboard Evaluation Results

Detailed results can be found here and on the ๐Ÿš€ Open Portuguese LLM Leaderboard

Metric Value
Average 66.05
ENEM Challenge (No Images) 69.63
BLUEX (No Images) 59.11
OAB Exams 49.61
Assin2 RTE 61.21
Assin2 STS 79.95
FaQuAD NLI 78.60
HateBR Binary 72.42
PT Hate Speech Binary 73.01
tweetSentBR 50.90

Comments

Any idea, help or report will always be welcome.

email: [email protected]

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