rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct

Phi-3-portuguese-tom-cat-128k-instruct

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 microsoft/Phi-3-mini-4k.

How to use

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 microsoft/Phi-3-mini-4k.

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 4b) 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/portuguese-Phi3-Tom-Cat-128k-instruct", device_map= {"": 0})
tokenizer = AutoTokenizer.from_pretrained("rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct")
model.eval()

You can use with Pipeline.

from transformers import pipeline
pipe = pipeline("text-generation",
                model=model,
                tokenizer=tokenizer,
                do_sample=True,
                max_new_tokens=512,
                num_beams=2,
                temperature=0.3,
                top_k=50,
                top_p=0.95,
                early_stopping=True,
                pad_token_id=tokenizer.eos_token_id,
                )


def format_template(question:str):
    system_prompt = "Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido."
    return f"""<s><|system|>
    { system_prompt }
    <|user|>
    { question }
    <|assistant|>
    """

question = format_template("E possivel ir de Carro dos Estados unidos ate o japão")
pipe(question)

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}
)

Open Portuguese LLM Leaderboard Evaluation Results

Detailed results can be found here and on the 🚀 Open Portuguese LLM Leaderboard

Metric	Value
Average	60.06
ENEM Challenge (No Images)	51.15
BLUEX (No Images)	42.56
OAB Exams	39.86
Assin2 RTE	88.86
Assin2 STS	68
FaQuAD NLI	45.16
HateBR Binary	85.92
PT Hate Speech Binary	65.76
tweetSentBR	53.32

Comments

Any idea, help or report will always be welcome.

email: rhaymisoncristian@gmail.com