license, datasets, language, metrics, library_name, tags, pipeline_tag, widget, inference, co2_eq_emissions, base_model

license	datasets	language	metrics	library_name	tags	pipeline_tag	widget	inference	co2_eq_emissions	base_model
apache-2.0	nicholasKluge/instruct-aira-dataset	pt	accuracy	transformers	alignment instruction tuned text generation conversation assistant	text-generation	text example_title <|startofinstruction|>Você pode me explicar o que é Aprendizagem de Máquina?<|endofinstruction|> Aprendizagem de Máquina text example_title <|startofinstruction|>Você sabe alguma coisa sobre Ética das Virtudes?<|endofinstruction|> Ética text example_title <|startofinstruction|>Como eu posso fazer a minha namorada feliz?<|endofinstruction|> Conselho	parameters repetition_penalty temperature top_k top_p max_new_tokens early_stopping 1.2 0.1 50 1.0 200 true	emissions source training_type geographical_location hardware_used 350 CodeCarbon fine-tuning Singapore NVIDIA A100-SXM4-40GB	pierreguillou/gpt2-small-portuguese

Aira-2-portuguese-124M

Aira-2 is the second version of the Aira instruction-tuned series. Aira-2-portuguese-124M is an instruction-tuned 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 source code used to train this model.

Usage

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

<|startofinstruction|>O que é um modelo de linguagem?<|endofinstruction|>Um modelo de linguagem é uma distribuição de probabilidade sobre um vocabulário.<|endofcompletion|>

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.sep_token,
  add_special_tokens=False,
  return_tensors="pt").to(device)

responses = aira.generate(**inputs,	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

• Hallucinations: This model can produce content that can be mistaken for truth but is, in fact, misleading or entirely false, i.e., hallucination.

• Biases and Toxicity: This model inherits the social and historical stereotypes from the data used to train it. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.

• Repetition and Verbosity: The model may get stuck on repetition loops (especially if the repetition penalty during generations is set to a meager value) or produce verbose responses unrelated to the prompt it was given.

Evaluation

Model	Average	ARC	TruthfulQA	ToxiGen
Aira-2-portuguese-124M	32.73	24.87	40.60	None
Gpt2-small-portuguese	31.96	22.48	41.44	None

• Evaluations were performed using the Language Model Evaluation Harness (by EleutherAI). The ToxiGen evaluation was not performed because the task is not available in Portuguese. Thanks to Laiviet for translating some of the tasks in the LM-Evaluation-Harness.

Cite as 🤗

@misc{nicholas22aira,
  doi = {10.5281/zenodo.6989727},
  url = {https://github.com/Nkluge-correa/Aira},
  author = {Nicholas Kluge Corrêa},
  title = {Aira},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
}

@phdthesis{kluge2024dynamic,
  title={Dynamic Normativity},
  author={Kluge Corr{\^e}a, Nicholas},
  year={2024},
  school={Universit{\"a}ts-und Landesbibliothek Bonn}
}

License

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