metadata

license: other
datasets:
  - nicholasKluge/instruct-aira-dataset
language:
  - en
metrics:
  - accuracy
library_name: transformers
tags:
  - alignment
  - instruction tuned
  - text generation
  - conversation
  - assistant
pipeline_tag: text-generation
widget:
  - text: Can you explain what is Machine Learning?<|endofinstruction|>
    example_title: Machine Learning
  - text: Do you know anything about virtue ethics?<|endofinstruction|>
    example_title: Ethics
  - text: How can I make my girlfriend happy?<|endofinstruction|>
    example_title: Advise
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: 0.25
  source: CodeCarbon
  training_type: fine-tuning
  geographical_location: Singapore
  hardware_used: NVIDIA A100-SXM4-40GB

Aira-OPT-125M

Aira-2 is the second version of the Aira instruction-tuned series. Aira-OPT-125M is an instruction-tuned model based on OPT. 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: 125,237,760 parameters
Dataset: Instruct-Aira Dataset
Language: English
Number of Epochs: 5
Batch size: 32
Optimizer: torch.optim.AdamW (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
GPU: 1 NVIDIA A100-SXM4-40GB
Emissions: 0.25 KgCO2 (Singapore)
Total Energy Consumption: 0.52 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-OPT-125M')
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-OPT-125M')

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: 👤 What is the capital of Brazil?

>>>Response 1: 🤖 The capital of Brazil is Brasília.
>>>Response 2: 🤖 The capital of Brazil is 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 (OPT)	Average	ARC	TruthfulQA	ToxiGen
Aira-OPT-125M	43.34	24.65	49.11	56.27
OPT-125M	40.29	22.78	42.88	55.21
Aira-OPT-350M	41.56	25.00	42.13	57.55
OPT-350M	40.62	23.97	41.00	56.91
Aira-OPT-1B3	43.90	28.41	46.59	56.70
OPT-1.3b	40.91	29.69	38.68	54.36

Evaluations were performed using the Language Model Evaluation Harness (by EleutherAI).

Cite as 🤗


@misc{nicholas22aira,
  doi = {10.5281/zenodo.6989727},
  url = {https://huggingface.co/nicholasKluge/Aira-OPT-125M},
  author = {Nicholas Kluge Corrêa},
  title = {Aira},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
}

nicholasKluge
/

Aira-OPT-125M