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_length: 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-portuguese-1B7 is the second version of the Aira instruction-tuned series. Aira is an instruction-tuned GPT-style 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 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:
<|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 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.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 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-560M},
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.