README.md

---
license: apache-2.0
datasets:
- Dahoas/synthetic-instruct-gptj-pairwise
- databricks/databricks-dolly-15k
- HuggingFaceH4/instruction-dataset
- nicholasKluge/instruct-aira-dataset
language:
- en
metrics:
- bleu
library_name: transformers
tags:
- alignment
- instruction tuned
- text generation
- conversation
- assistant
pipeline_tag: text-generation
widget:
- text: <|startoftext|>Hello! What is your name?<|endoftext|>
  example_title: Greetings
- text: <|startoftext|>Can you explain what is Machine Learning?<|endoftext|>
  example_title: Machine Learning
- text: <|startoftext|>Do you know anything about virtue ethics?<|endoftext|>
  example_title: Ethics
- text: <|startoftext|>How can I make my girlfried happy?<|endoftext|>
  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
model-index:
- name: Aira-Instruct-774M
  results:
  - task:
      type: text-generation
    dataset:
      type: text-generation
      name: truthful_qa
    metrics:
    - name: rouge
      type: rouge
      value: 0.23884372491125055
      verified: false
co2_eq_emissions:
  emissions: 0_003
  source: "CodeCarbon"
  training_type: "fine-tuning"
  geographical_location: "Canada"
  hardware_used: "NVIDIA A100-SXM4-40GB"
---
# Aira-Instruct-774M

`Aira-Instruct-774M` is a instruction-tuned GPT-style model based on [GPT-2](https://huggingface.co/gpt2). The model was trained with a dataset composed of `prompt`, `completions`, generated via the [Self-Instruct](https://github.com/yizhongw/self-instruct) framework. `Aira-Instruct-774M` instruction-tuning was achieved via conditional text generation.

The dataset used to train this model combines the following sources of data: the [`synthetic-instruct-gptj-pairwise`](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) dataset, the [`databricks_dolly_15k`](https://huggingface.co/datasets/HuggingFaceH4/databricks_dolly_15k) dataset, the [`instruction-dataset`](https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset) dataset, and a subset of [Aira's](https://github.com/Nkluge-correa/Aira-EXPERT) fine-tuning dataset, focused on Q&A related to Ethics, AI, AI safety, and other related topics. The dataset is available in both Portuguese and English.

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:** 0.0030 KgCO2 (Canada)
- **Total Energy Consumption:** 1.29 kWh

| Epoch/Loss|Training|Validation|
|---|---|---|
| 1 |0.696885|0.638819|
| 2 |0.516360|0.610071|
| 3 |0.338896|0.647381|

This repository has the notebook used to train this model.

## Usage

Two special tokens are used to mark the user side of the interaction and the model's response:

`<|startoftext|>`What is a language model?`<|endoftext|>`A language model is a probability distribution over a vocabulary.`<|endoftext|>`

```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-Instruct-774M')
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-Instruct-774M')

aira.eval()
aira.to(device)

question =  input("Enter your question: ")

inputs = tokenizer(tokenizer.bos_token + question + tokenizer.eos_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=200,
	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: 👤 Hello! What is your name?

>>>Response 1: 🤖 Hi there! I am Aira, a chatbot designed to answer questions about AI ethics and AI safety. If you need assistance navigating our conversation, please feel free to ask!
>>>Response 2: 🤖 Hi there! My name is Aira, and I'm a chatbot designed to answer questions related to AI ethics and AI Safety. If you need assistance, feel free to ask, and I'll be happy to help you out.
```

## 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.

## Cite as 🤗

```latex

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

```

## License

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