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---
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: 0.29
source: CodeCarbon
training_type: fine-tuning
geographical_location: United States of America
hardware_used: NVIDIA A100-SXM4-40GB
license: apache-2.0
---
# Aira-2-355M
`Aira-2` is the second version of the Aira instruction-tuned series. `Aira-2-355M` is an instruction-tuned model based on [GPT-2](https://huggingface.co/gpt2-medium). 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:** 354,825,216 parameters
- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset)
- **Language:** English
- **Number of Epochs:** 3
- **Batch size:** 16
- **Optimizer:** `torch.optim.AdamW` (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
- **GPU:** 1 NVIDIA A100-SXM4-40GB
- **Emissions:** 0.29 KgCO2 (United States of America)
- **Total Energy Consumption:** 0.83 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-2-355M')
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-355M')
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:
```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 (GPT-2) |Average |[ARC](https://arxiv.org/abs/1803.05457) |[TruthfulQA](https://arxiv.org/abs/2109.07958) |[ToxiGen](https://arxiv.org/abs/2203.09509) |
| ---------------------------------------------------------------------- | -------- | -------------------------------------- | --------------------------------------------- | ------------------------------------------ |
|[Aira-2-124M-DPO](https://huggingface.co/nicholasKluge/Aira-2-124M-DPO) |**40.68** |**24.66** |**42.61** |**54.79** |
|[Aira-2-124M](https://huggingface.co/nicholasKluge/Aira-2-124M) |38.07 |24.57 |41.02 |48.62 |
|GPT-2 |35.37 |21.84 |40.67 |43.62 |
|[Aira-2-355M](https://huggingface.co/nicholasKluge/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](https://huggingface.co/nicholasKluge/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](https://huggingface.co/nicholasKluge/Aira-2-1B5) |**42.22** |28.92 |**41.16** |**56.60** |
|GPT-2-xl |36.84 |**30.29** |38.54 |41.70 |
* 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-2-355M},
author = {Nicholas Kluge Corrêa},
title = {Aira},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
}
```
## License
The `Aira-2-355M` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nicholasKluge__Aira-2-355M)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 27.0 |
| ARC (25-shot) | 27.56 |
| HellaSwag (10-shot) | 38.92 |
| MMLU (5-shot) | 27.26 |
| TruthfulQA (0-shot) | 38.53 |
| Winogrande (5-shot) | 53.75 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 2.99 |
|