pt license: apache-2.0 library_name: transformers tags:
portugues
portuguese
QA
instruct
phi base_model: microsoft/Phi-3-mini-4k-instruct datasets:
rhaymison/superset pipeline_tag: text-generation model-index:
name: phi-3-portuguese-tom-cat-4k-instruct 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: 61.58 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 50.63 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 43.69 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 91.54 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 75.27 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 47.46 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 83.01 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 70.19 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia/tweetsentbr_fewshot split: test args: num_few_shot: 25 metrics:
  - type: f1_macro value: 57.78 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard

Phi-3-portuguese-tom-cat-4k-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.

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:

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 4b) 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/phi-3-portuguese-tom-cat-4k-instruct", device_map= {"": 0})
tokenizer = AutoTokenizer.from_pretrained("rhaymison/phi-3-portuguese-tom-cat-4k-instruct")
model.eval()

You can use with Pipeline.

python

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

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}
)

Open Portuguese LLM Leaderboard Evaluation Results

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

Metric	Value
Average	64.57
ENEM Challenge (No Images)	61.58
BLUEX (No Images)	50.63
OAB Exams	43.69
Assin2 RTE	91.54
Assin2 STS	75.27
FaQuAD NLI	47.46
HateBR Binary	83.01
PT Hate Speech Binary	70.19
tweetSentBR	57.78

Comments

Any idea, help or report will always be welcome.

email: rhaymisoncristian@gmail.com

RichardErkhov/rhaymison_-_phi-3-portuguese-tom-cat-4k-instruct-8bits

Original model description:

Phi-3-portuguese-tom-cat-4k-instruct

How to use

FULL MODEL : A100

HALF MODEL: L4

8bit or 4bit : T4 or V100

4bits example

Open Portuguese LLM Leaderboard Evaluation Results

Comments

RichardErkhov/
rhaymison_-_phi-3-portuguese-tom-cat-4k-instruct-8bits