metadata

license: apache-2.0
datasets:
  - nicholasKluge/instruct-aira-dataset
language:
  - pt
metrics:
  - accuracy
library_name: transformers
tags:
  - alignment
  - instruction tuned
  - text generation
  - conversation
  - assistant
pipeline_tag: text-generation
widget:
  - text: <|startofinstruction|>Olá! Como você se chama?<|endofinstruction|>
    example_title: Olá
  - text: >-
      <|startofinstruction|>Você pode me explicar o que é Aprendizagem de
      Máquina?<|endofinstruction|>
    example_title: Aprendizagem de Máquina
  - text: >-
      <|startofinstruction|>Você sabe alguma coisa sobre Ética das
      Virtudes?<|endofinstruction|>
    example_title: Ética
  - text: >-
      <|startofinstruction|>Como eu posso fazer a minha namorada
      feliz?<|endofinstruction|>
    example_title: Conselho
inference:
  parameters:
    repetition_penalty: 1.2
    temperature: 0.2
    top_k: 30
    top_p: 0.3
    max_length: 200
    length_penalty: 0.3
    early_stopping: true
co2_eq_emissions:
  emissions: 0.35
  source: CodeCarbon
  training_type: fine-tuning
  geographical_location: Singapore
  hardware_used: NVIDIA A100-SXM4-40GB

Aira-2-portuguese-124M

Aira-2-portuguese-124M is the second version of the Aira instruction-tuned series. iAira is an instruction-tuned GPT-style model based on GPT-2. The model was trained with a dataset composed of prompt, completions generated synthetically by prompting already-tuned models (ChatGPT, Llama, Open-Assistant, etc).

Check our gradio-demo in Spaces.

Details

Size: 124,441,344 parameters
Dataset: Instruct-Aira Dataset
Language: Portuguese
Number of Epochs: 5
Batch size: 24
Optimizer: torch.optim.AdamW (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
GPU: 1 NVIDIA A100-SXM4-40GB
Emissions: 0.35 KgCO2 (Singapore)
Total Energy Consumption: 0.73 kWh

This repository has the notebook used to train this model.

Usage

Three special tokens are used to mark the user side of the interaction and the model's response:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-portuguese-124M')
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-portuguese-124M')

aira.eval()
aira.to(device)

question =  input("Enter your question: ")

inputs = tokenizer(tokenizer.bos_token + question + tokenizer.eos_token, return_tensors="pt").to(device)

responses = aira.generate(**inputs,
    bos_token_id=tokenizer.bos_token_id,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
    do_sample=True,
    top_k=50,
    max_length=200,
    top_p=0.95,
    temperature=0.7,
    num_return_sequences=2)

print(f"Question: 👤 {question}\n")

for i, response in  enumerate(responses):
    print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, "")}')

The model will output something like:

>>> Question: 👤 Qual a capital do Brasil?

>>>Response 1: 🤖 A capital do Brasil é Brasília.
>>>Response 2: 🤖 A capital do Brasil é Brasília.

Limitations

🤥 Generative models can perpetuate the generation of pseudo-informative content, that is, false information that may appear truthful.

🤬 In certain types of tasks, generative models can produce harmful and discriminatory content inspired by historical stereotypes.

Cite as 🤗


@misc{nicholas22aira,
  doi = {10.5281/zenodo.6989727},
  url = {https://huggingface.co/nicholasKluge/Aira-Instruct-PT-124M},
  author = {Nicholas Kluge Corrêa and Carolina Del Pino},
  title = {Aira},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
}

License

The Aira-2-portuguese-124M is licensed under the Apache License, Version 2.0. See the LICENSE file for more details.