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_new_tokens: 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
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
🤥 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.
Evaluation
Model (gpt2-portuguese) | Average | ARC | TruthfulQA | ToxiGen |
---|---|---|---|---|
Aira-2-portuguese-124M | 34.73 | 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://huggingface.co/nicholasKluge/Aira-2-portuguese-124M},
author = {Nicholas Kluge Corrêa},
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.