README.md

---
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: "What is your name?<|endofinstruction|>"
  example_title: Greetings
- 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 OPT-style model based on [OPT](https://huggingface.co/facebook/opt-125m). 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](https://huggingface.co/spaces/nicholasKluge/Aira-Demo).

## Details

- **Size:** 125,237,760 parameters
- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/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](https://github.com/Nkluge-correa/Aira) 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|>`

```python
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,
	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:

```markdown
>>>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](https://arxiv.org/abs/1803.05457) | [TruthfulQA](https://arxiv.org/abs/2109.07958) | [ToxiGen](https://arxiv.org/abs/2203.09509) |   |   |
|---------------------------------------------------------------------|-----------|-----------------------------------------|------------------------------------------------|---------------------------------------------|---|---|
| [Aira-OPT-125M](https://huggingface.co/nicholasKluge/Aira-OPT-125M) | **43.34** | **24.65**                               | **49.11**                                      | **56.27**                                   |   |   |
| OPT-125M                                                            | 40.29     | 22.78                                   | 42.88                                          | 55.21                                       |   |   |
| [Aira-OPT-350M](https://huggingface.co/nicholasKluge/Aira-OPT-350M) | **41.56** | **25.00**                               | **42.13**                                      | **57.55**                                   |   |   |
| OPT-350M                                                            | 40.62     | 23.97                                   | 41.00                                          | 56.91                                       |   |   |
| [Aira-OPT-1B3](https://huggingface.co/nicholasKluge/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](https://github.com/EleutherAI/lm-evaluation-harness) (by [EleutherAI](https://www.eleuther.ai/)).

## Cite as 🤗

```latex

@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},
}

```

## License

The `Aira-OPT-125M` is licensed under the OPT-175B License Agreement, Copyright (c) Meta Platforms, Inc. All Rights Reserved. See the [LICENSE](LICENSE.md) file for more details.