language, license, library_name, tags, base_model, datasets, pipeline_tag, model-index

language	license	library_name	tags	base_model	datasets	pipeline_tag	model-index
pt	apache-2.0	transformers	Misral Portuguese 7b	mistralai/Mistral-7B-Instruct-v0.2	pablo-moreira/gpt4all-j-prompt-generations-pt rhaymison/superset	text-generation	name results Mistral-portuguese-luana-7b task dataset metrics source type name text-generation Text Generation name type split args ENEM Challenge (No Images) eduagarcia/enem_challenge train num_few_shot 3 type value name acc 58.64 accuracy url name https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b Open Portuguese LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args BLUEX (No Images) eduagarcia-temp/BLUEX_without_images train num_few_shot 3 type value name acc 47.98 accuracy url name https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b Open Portuguese LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args OAB Exams eduagarcia/oab_exams train num_few_shot 3 type value name acc 38.82 accuracy url name https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b Open Portuguese LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args Assin2 RTE assin2 test num_few_shot 15 type value name f1_macro 90.63 f1-macro url name https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b Open Portuguese LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args Assin2 STS eduagarcia/portuguese_benchmark test num_few_shot 15 type value name pearson 75.81 pearson url name https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b Open Portuguese LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args FaQuAD NLI ruanchaves/faquad-nli test num_few_shot 15 type value name f1_macro 57.79 f1-macro url name https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b Open Portuguese LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HateBR Binary ruanchaves/hatebr test num_few_shot 25 type value name f1_macro 77.24 f1-macro url name https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b Open Portuguese LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args PT Hate Speech Binary hate_speech_portuguese test num_few_shot 25 type value name f1_macro 68.5 f1-macro url name https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b Open Portuguese LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args tweetSentBR eduagarcia-temp/tweetsentbr test num_few_shot 25 type value name f1_macro 63.0 f1-macro url name https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b 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
• 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.

!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

inputs = tokenizer([f"""<s>[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

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

Detailed results can be found here

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