metadata

license: bigscience-bloom-rail-1.0
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|>Como você se chama?<|endofinstruction|>
    example_title: Saudações
  - text: >-
      <|startofinstruction|>Me explique o que é Aprendizagem de
      Máquina?<|endofinstruction|>
    example_title: Aprendizagem de Máquina
  - text: >-
      <|startofinstruction|>Você sabe alguma coisa sobre a 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: 100
    length_penalty: 0.3
    early_stopping: true
co2_eq_emissions:
  emissions: 1.99
  source: CodeCarbon
  training_type: fine-tuning
  geographical_location: Singapore
  hardware_used: NVIDIA A100-SXM4-40GB

Aira-2-portuguese-1B7

Aira-2 is the second version of the Aira instruction-tuned series. Aira-2-portuguese-1B7 is an instruction-tuned model based on BLOOM. The model was trained with a dataset composed of prompts and completions generated synthetically by prompting already-tuned models (ChatGPT, Llama, Open-Assistant, etc).

Check our gradio-demo in Spaces.

Details

Size: 1,722,005,504 parameters
Dataset: Instruct-Aira Dataset
Language: Portuguese
Number of Epochs: 3
Batch size: 4
Optimizer: torch.optim.AdamW (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
GPU: 1 NVIDIA A100-SXM4-40GB
Emissions: 1.99 KgCO2 (Singapore)
Total Energy Consumption: 4.09 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 AutoModelForCausalLM, AutoTokenizer
import torch

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

tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-portuguese-1B7')
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-portuguese-1B7')

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,
    do_sample=True,
    top_k=50,
    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 da Alemanha?

>>>Response 1: 🤖 A capital da Alemanha é Berlim. É a maior cidade da Alemanha e serve como centro administrativo, cultural e político da Alemanha.
>>>Response 2: 🤖 A capital da Alemanha é Berlim. É a maior cidade da Alemanha e serve como centro administrativo, cultural e político da Alemanha.

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-2-portuguese-1B7},
  author = {Nicholas Kluge Corrêa},
  title = {Aira},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
}

License

The Aira-2-portuguese-1B7 is licensed under the RAIL License since it is a model derived from BLOOM. See the LICENSE file for more details.