pt license: apache-2.0 library_name: transformers tags:
Misral
Portuguese
7b
chat
portugues base_model: Qwen/Qwen1.5-7B datasets:
rhaymison/superset pipeline_tag: text-generation inference: false model-index:
name: Qwen-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: 58.36 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-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: 48.12 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-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: 42.73 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-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: 81.05 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-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: 74.25 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-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: 57.96 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-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: 70.29 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-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: 69.92 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-portuguese-luana-7b 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: 59.69 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-portuguese-luana-7b name: Open Portuguese LLM Leaderboard

Qwen-portuguese-luana-7b

This model was trained with a superset of 250,000 chat in Portuguese. The model comes to help fill the gap in models in Portuguese. Tuned from the Qwen1.5-7B-Chat.

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
from transformers import pipeline
model = AutoModelForCausalLM.from_pretrained("rhaymison/Qwen-portuguese-luana-7b", device_map= {"": 0})
tokenizer = AutoTokenizer.from_pretrained("rhaymison/Qwen-portuguese-luana-7b")
model.eval()

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

python

prompt = f"""<|im_start|>system
Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.<|im_end|>
<|im_start|>user
 ### instrução: Me indique uma programação para fazer no final de semana com minha esposa. <|im_end|>
<|im_start|>"""

pipe = pipeline("text-generation",
                model=model,
                tokenizer=tokenizer,
                do_sample=True,
                max_new_tokens=200,
                num_beams=2,
                temperature=0.3,
                top_k=50,
                top_p=0.95,
                early_stopping=True,
                pad_token_id=tokenizer.eos_token_id,
                )

pipe(prompt)[0]['generated_text'].split('assistant')[1]

#Claro! Aqui está uma sugestão de programação para o final de semana com sua esposa:
#Domingo:
#1. Despertar cedo para um café da manhã delicioso juntos.
#2. Faça uma caminhada ou uma corrida no parque local para aproveitar o ar fresco e a natureza.
#3. Depois do café da manhã, faça uma caminhada de compras para escolher algumas roupas ou acessórios novos.
#4. Retorne para casa para preparar uma refeição deliciosa juntos.
#5. Depois do almoço, você pode assistir a um filme ou jogar jogos de tabuleiro para relaxar

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	62.49
ENEM Challenge (No Images)	58.36
BLUEX (No Images)	48.12
OAB Exams	42.73
Assin2 RTE	81.05
Assin2 STS	74.25
FaQuAD NLI	57.96
HateBR Binary	70.29
PT Hate Speech Binary	69.92
tweetSentBR	59.69

Comments

Any idea, help or report will always be welcome.

email: rhaymisoncristian@gmail.com

RichardErkhov/rhaymison_-_Qwen-portuguese-luana-7b-8bits

Original model description:

Qwen-portuguese-luana-7b

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_-_Qwen-portuguese-luana-7b-8bits