README.md

---
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: 69.0
      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

<p align="center">
  <img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/llama3-luana.webp"  width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
</p>


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, 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.

```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/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.
```python

from transformers import pipeline
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,
                pad_token_id=tokenizer.eos_token_id,
                )


def format_prompt(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_prompt("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

```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](https://huggingface.co./datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/Llama3-portuguese-luana-8b-instruct) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co./spaces/eduagarcia/open_pt_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]

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