Original model description:

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|>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_new_tokens: 200 length_penalty: 0.3 early_stopping: true co2_eq_emissions: emissions: 350 source: CodeCarbon training_type: fine-tuning geographical_location: Singapore hardware_used: NVIDIA A100-SXM4-40GB

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:

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.