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Aira-2-774M

Text Generation
Transformers
PyTorch
Safetensors
English
gpt2
alignment
instruction tuned
text generation
conversation
assistant
text-generation-inference
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metadata
license: apache-2.0
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: >-
      <|startofinstruction|>Can you explain what is Machine
      Learning?<|endofinstruction|>
    example_title: Machine Learning
  - text: >-
      <|startofinstruction|>Do you know anything about virtue
      ethics?<|endofinstruction|>
    example_title: Ethics
  - text: >-
      <|startofinstruction|>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: 0.77
  source: CodeCarbon
  training_type: fine-tuning
  geographical_location: United States of America
  hardware_used: NVIDIA A100-SXM4-40GB

Aira-2-774M

Aira-2 is the second version of the Aira instruction-tuned series. Aira-2-774M is an instruction-tuned model based on GPT-2. 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: 774,032,640 parameters
  • Dataset: Instruct-Aira Dataset
  • Language: English
  • Number of Epochs: 3
  • Batch size: 8
  • Optimizer: torch.optim.AdamW (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
  • GPU: 1 NVIDIA A100-SXM4-40GB
  • Emissions: 0.77 KgCO2 (Singapore)
  • Total Energy Consumption: 1.58 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|>What is a language model?<|endofinstruction|>A language model is a probability distribution over a vocabulary.<|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-2-774M')
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-774M')

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 (GPT-2) Average ARC TruthfulQA ToxiGen
Aira-2-124M-DPO 40.68 24.66 42.61 54.79
Aira-2-124M 38.07 24.57 41.02 48.62
GPT-2 35.37 21.84 40.67 43.62
Aira-2-355M 39.68 27.56 38.53 53.19
GPT-2-medium 36.43 27.05 40.76 41.49
Aira-2-774M 42.26 28.75 41.33 56.70
GPT-2-large 35.16 25.94 38.71 40.85
Aira-2-1B5 42.22 28.92 41.16 56.60
GPT-2-xl 36.84 30.29 38.54 41.70

Cite as 🤗 


@misc{nicholas22aira,
  doi = {10.5281/zenodo.6989727},
  url = {https://huggingface.co/nicholasKluge/Aira-2-774M},
  author = {Nicholas Kluge Corrêa},
  title = {Aira},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
}

License

The Aira-2-774M is licensed under the Apache License, Version 2.0. See the LICENSE file for more details.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 27.47
ARC (25-shot) 28.75
HellaSwag (10-shot) 40.8
MMLU (5-shot) 25.1
TruthfulQA (0-shot) 41.33
Winogrande (5-shot) 52.01
GSM8K (5-shot) 0.0
DROP (3-shot) 4.26