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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|>What is your name?<|endofinstruction|>
example_title: Greetings
- 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_length: 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 GPT-style 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 [notebook](AIRA_FineTuning.ipynb) 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, 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=500,
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 (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](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/)). The notebook used to make these evaluations is available in the [this repo](lm_evaluation_harness.ipynb).
## 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 |
|