File size: 6,878 Bytes
df5c880
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0432450
df5c880
 
 
 
 
 
 
 
 
 
 
4638706
df5c880
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fb5db93
df5c880
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9a7f11a
 
 
df5c880
af2c5d5
df5c880
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4638706
df5c880
4638706
 
 
df5c880
 
 
3bfd3c8
 
 
 
 
 
 
 
 
 
 
df5c880
fb5db93
b725d89
df5c880
 
 
 
 
 
04e7aa8
 
df5c880
 
 
 
 
 
 
 
 
 
4638706
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
---
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](https://huggingface.co/gpt2-large). 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:** 774,032,640 parameters
- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/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](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-2-774M')
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:

```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

- **Hallucinations:** This model can produce content that can be mistaken for truth but is, in fact, misleading or entirely false, i.e., hallucination.

- **Biases and Toxicity:** This model inherits the social and historical stereotypes from the data used to train it. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.

- **Repetition and Verbosity:** The model may get stuck on repetition loops (especially if the repetition penalty during generations is set to a meager value) or produce verbose responses unrelated to the prompt it was given.

## 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-2-774M},
  author = {Nicholas Kluge Corrêa},
  title = {Aira},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
}

```

## License

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