metadata
language:
- pt
license: apache-2.0
library_name: transformers
tags:
- portugues
- portuguese
- QA
- instruct
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- rhaymison/superset
pipeline_tag: text-generation
model-index:
- name: Llama3-portuguese-luana-8b-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: 69
name: accuracy
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-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: 51.74
name: accuracy
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-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: 47.56
name: accuracy
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-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: 89.24
name: f1-macro
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-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: 72.87
name: pearson
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-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: 68.94
name: f1-macro
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-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: 85.93
name: f1-macro
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-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: 64.16
name: f1-macro
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-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: 63.91
name: f1-macro
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct
name: Open Portuguese LLM Leaderboard
Llama3-portuguese-luana-8b-instruct
This model was trained with a superset of 290,000 chat in Portuguese. The model comes to help fill the gap in models in Portuguese. Tuned from the Llama3 8B in Portuguese, the model was adjusted mainly for 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 8b) 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/Llama3-portuguese-luana-8b-instruct", device_map= {"": 0})
tokenizer = AutoTokenizer.from_pretrained("rhaymison/Llama3-portuguese-luana-8b-instruct")
model.eval()
You can use with Pipeline.
from transformers import pipeline
stop_token = "<|eot_id|>"
stop_token_id = tokenizer.encode(stop_token)[0]
pipe = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
do_sample=True,
max_new_tokens=256,
num_beams=2,
temperature=0.3,
top_k=50,
top_p=0.95,
early_stopping=True,
eos_token_id=stop_token_id,
pad_token_id=tokenizer.eos_token_id,
)
def format_dataset(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"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{ system_prompt }<|eot_id|><|start_header_id|>user<|end_header_id|>
{ question }<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
prompt = format_dataset("Me explique quem eram os Romanos")
result = pipe(prompt)
result[0]["generated_text"].split("assistant<|end_header_id|>")[1]
#Os romanos eram um povo antigo que habitava a península italiana, particularmente na região que hoje é conhecida como Itália. Eles estabeleceram o Império Romano,
#que se tornou uma das maiores e mais poderosas civilizações da história. Os romanos eram conhecidos por suas conquistas militares, sua arquitetura e engenharia
#impressionantes e sua influência duradoura na cultura ocidental.
#Os romanos eram uma sociedade complexa que consistia em várias classes sociais, incluindo senadores, cavaleiros, plebeus e escravos.
#Eles tinham um sistema de governo baseado em uma república, onde o poder era dividido entre o Senado e a Assembléia do Povo.
#Os romanos eram conhecidos por suas conquistas militares, que os levaram a expandir seu império por toda a Europa, Ásia e África.
#Eles estabeleceram uma rede de estradas, pontes e outras estruturas que facilitaram a comunicação e o comércio.
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 | 68.15 |
| ENEM Challenge (No Images) | 69 |
| BLUEX (No Images) | 51.74 |
| OAB Exams | 47.56 |
| Assin2 RTE | 89.24 |
| Assin2 STS | 72.87 |
| FaQuAD NLI | 68.94 |
| HateBR Binary | 85.93 |
| PT Hate Speech Binary | 64.16 |
| tweetSentBR | 63.91 |
Comments
Any idea, help or report will always be welcome.
email: [email protected]
