# Aira-2-portuguese-560M

Aira-2 is the second version of the Aira instruction-tuned series. Aira-2-portuguese-560M is an instruction-tuned model based on [BLOOM](https://huggingface.co/bigscience/bloom-560m). 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-Portuguese).

## Details

- **Size:** 559,012,864 parameters
- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset)
- **Language:** Portuguese
- **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:** 0.80 KgCO2 (Singapore)
- **Total Energy Consumption:** 1.64 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|>`O que é um modelo de linguagem?`<|endofinstruction|>`Um modelo de linguagem é uma distribuição de probabilidade sobre um vocabulário.`<|endofcompletion|>`

```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-2-portuguese-560M')
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-portuguese-560M')

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: 👤 Qual a capital da Alemanha?

>>>Response 1: 🤖 A capital da Alemanha é Berlim. É a maior cidade da Alemanha e serve como centro administrativo, cultural e político da Alemanha.
>>>Response 2: 🤖 A capital da Alemanha é Berlim. É a maior cidade da Alemanha e serve como centro administrativo, cultural e político da Alemanha.
```

## 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.

## Cite as 🤗

```latex

@misc{nicholas22aira,
  doi = {10.5281/zenodo.6989727},
  url = {https://huggingface.co/nicholasKluge/Aira-2-portuguese-560M},
  author = {Nicholas Kluge Corrêa},
  title = {Aira},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
}

```

## License

Aira-2-portuguese-560M is licensed under the RAIL License since it is a model derived from BLOOM. See the [LICENSE](LICENSE) file for more details.
{"language": ["pt"], "license": "bigscience-bloom-rail-1.0", "library_name": "transformers", "tags": ["alignment", "instruction tuned", "text generation", "conversation", "assistant"], "datasets": ["nicholasKluge/instruct-aira-dataset"], "metrics": ["accuracy"], "pipeline_tag": "text-generation", "widget": [{"text": "<|startofinstruction|>Me explique o que \u00e9 Aprendizagem de M\u00e1quina?<|endofinstruction|>", "example_title": "Aprendizagem de M\u00e1quina"}, {"text": "<|startofinstruction|>Voc\u00ea sabe alguma coisa sobre a \u00c9tica das Virtudes?<|endofinstruction|>", "example_title": "\u00c9tica"}, {"text": "<|startofinstruction|>Como eu posso fazer a minha namorada feliz?<|endofinstruction|>", "example_title": "Conselho"}], "inference": {"parameters": {"repetition_penalty": 1.2, "temperature": 0.2, "top_k": 30, "top_p": 0.3, "max_new_tokens": 100, "length_penalty": 0.3, "early_stopping": true}}, "co2_eq_emissions": {"emissions": 0.8, "source": "CodeCarbon", "training_type": "fine-tuning", "geographical_location": "Singapore", "hardware_used": "NVIDIA A100-SXM4-40GB"}}
transformers pytorch safetensors bloom text-generation alignment instruction tuned text generation conversation assistant pt dataset:nicholasKluge/instruct-aira-dataset license:bigscience-bloom-rail-1.0 co2_eq_emissions autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #bloom #text-generation #alignment #instruction tuned #text generation #conversation #assistant #pt #dataset-nicholasKluge/instruct-aira-dataset #license-bigscience-bloom-rail-1.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# Aira-2-portuguese-560M

Aira-2 is the second version of the Aira instruction-tuned series. Aira-2-portuguese-560M is an instruction-tuned model based on BLOOM. 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.

## Details

- Size: 559,012,864 parameters
- Dataset: Instruct-Aira Dataset
- Language: Portuguese
- Number of Epochs: 3
- Batch size: 8
- Optimizer: 'URL.AdamW' (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
- GPU: 1 NVIDIA A100-SXM4-40GB
- Emissions: 0.80 KgCO2 (Singapore)
- Total Energy Consumption: 1.64 kWh

This repository has the source code 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|>'O que é um modelo de linguagem?'<|endofinstruction|>'Um modelo de linguagem é uma distribuição de probabilidade sobre um vocabulário.'<|endofcompletion|>'



The model will output something like:



## 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.

## Cite as 



## License

Aira-2-portuguese-560M is licensed under the RAIL License since it is a model derived from BLOOM. See the LICENSE file for more details.
# Aira-2-portuguese-560M

Aira-2 is the second version of the Aira instruction-tuned series. Aira-2-portuguese-560M is an instruction-tuned model based on BLOOM. 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. ## Details

- Size: 559,012,864 parameters
- Dataset: Instruct-Aira Dataset
- Language: Portuguese
- Number of Epochs: 3
- Batch size: 8
- Optimizer: 'URL.AdamW' (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
- GPU: 1 NVIDIA A100-SXM4-40GB
- Emissions: 0.80 KgCO2 (Singapore)
- Total Energy Consumption: 1.64 kWh

This repository has the source code 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|>'O que é um modelo de linguagem?'<|endofinstruction|>'Um modelo de linguagem é uma distribuição de probabilidade sobre um vocabulário.'<|endofcompletion|>'



The model will output something like: ## 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. ## Cite as ## License

Aira-2-portuguese-560M is licensed under the RAIL License since it is a model derived from BLOOM. See the LICENSE file for more details.
TAGS
#transformers #pytorch #safetensors #bloom #text-generation #alignment #instruction tuned #text generation #conversation #assistant #pt #dataset-nicholasKluge/instruct-aira-dataset #license-bigscience-bloom-rail-1.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Aira-2-portuguese-560M

Aira-2 is the second version of the Aira instruction-tuned series. Aira-2-portuguese-560M is an instruction-tuned model based on BLOOM. 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. ## Details

- Size: 559,012,864 parameters
- Dataset: Instruct-Aira Dataset
- Language: Portuguese
- Number of Epochs: 3
- Batch size: 8
- Optimizer: 'URL.AdamW' (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
- GPU: 1 NVIDIA A100-SXM4-40GB
- Emissions: 0.80 KgCO2 (Singapore)
- Total Energy Consumption: 1.64 kWh

This repository has the source code 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|>'O que é um modelo de linguagem?'<|endofinstruction|>'Um modelo de linguagem é uma distribuição de probabilidade sobre um vocabulário.'<|endofcompletion|>'



The model will output something like: ## 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. ## Cite as ## License

Aira-2-portuguese-560M is licensed under the RAIL License since it is a model derived from BLOOM. See the LICENSE file for more details.