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---
license: bigscience-bloom-rail-1.0
datasets:
- nicholasKluge/fine-tuning-instruct-aira
- Dahoas/synthetic-instruct-gptj-pairwise
- databricks/databricks-dolly-15k
- HuggingFaceH4/instruction-dataset
language:
- pt
metrics:
- bleu
library_name: transformers
tags:
- alignment
- instruction tuned
- text generation
- conversation
- assistant
pipeline_tag: text-generation
widget:
- text: <|startoftext|>Olá! Qual o seu nome?<|endoftext|>
example_title: Olá
- text: >-
<|startoftext|>Você pode me explicar o que é aprendizagem de
máquina?<|endoftext|>
example_title: Aprendizagem de máquina
- text: <|startoftext|>Você sabe alguma coisa sobre ética das virtudes<|endoftext|>
example_title: Ética das virtudes
- text: <|startoftext|>O que posso fazer para alegrar minha namorada?<|endoftext|>
example_title: Conselho
inference:
parameters:
repetition_penalty: 1.2
temperature: 0.2
top_k: 30
top_p: 0.3
max_length: 100
length_penalty: 0.3
early_stopping: True
---
# Aira-Instruct-PT-1B7 (Portuguese)
`Aira-Instruct-PT-1B7` is a instruction-tuned GPT-style model based on [BLOOM](https://huggingface.co/bigscience/bloom-1b7). 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-PT-1B7` 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-Portuguese).
## Details
- **Size:** 559,012,864 total parameters
- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/fine-tuning-instruct-aira)
- **Language:** Portuguese
- **Number of Epochs:** 2
- **Batch size:** 6
- **Optimizer:** `torch.optim.AdamW` (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
- **GPU:** 1 NVIDIA A100-SXM4-40GB
| Epoch|Training Loss|Validation Loss|
|---|---|---|
| 1 |0.888486|0.744728|
| 2 |0.749612|0.673719|
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 AutoModelForCausalLM, AutoTokenizer
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-Instruct-PT-1B7')
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-Instruct-PT-1B7')
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: 👤 Olá! Como você se chama?
>>>Response 1: 🤖 Olá! Meu nome é Aira e sou um chatbot projetado para conversar sobre Ética e Segurança da IA. Se você precisar de ajuda com um assunto diferente, por favor, peça "ajuda".
>>>Response 2: 🤖 Olá! Meu nome é Aira e sou um chatbot treinado para responder perguntas sobre Ética e Segurança da IA. Se você precisar de ajuda para navegar em nossa conversa, não hesite em pedir ajuda.
```
## 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-PT-560M},
author = {Nicholas Kluge Corrêa and Carolina Del Pino},
title = {Aira},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
}
```
## License
The `Aira-Instruct-PT-1B7` is licensed under the RAIL License since it is a model derived from BLOOM. See the [LICENSE](LICENSE) file for more details.
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