Tucano-1b1-Instruct

Model Summary

Tucano-1b1-Instruct is a fine-tuned version of Tucano-1b1. Tucano is a series of decoder-transformers natively pretrained in Portuguese. All Tucano models were trained on GigaVerbo, a concatenation of deduplicated Portuguese text corpora amounting to 200 billion tokens.

The fine-tuning process was divided into two stages:

Supervised fine-tuning (SFT) using the TucanoBR/Tucano-SFT, a concatenation of three different instruction tuning datasets (cnmoro/GPT4-500k-Augmented-PTBR-Clean, rhaymison/orca-math-portuguese-64k, nicholasKluge/instruct-aira-dataset-v3).
Direct Preference Optimization (DPO) using the nicholasKluge/reward-aira-dataset.

Read our preprint here.

Details

Architecture: a Transformer-based model pre-trained via causal language modeling
Size: 1,100,048,384 parameters
Context length: 2048 tokens
Dataset:
Language: Portuguese
Training time: ~ 12 hours
Emissions: 22 KgCO2 (Germany)
Total energy consumption: 58 kWh

This repository has the source code used to train this model. The main libraries used are:

Intended Uses

The primary intended use of the Tucano models is to serve as foundations for research and development involving native Portuguese language modeling. Checkpoints saved during training are designed to provide a controlled setting for performing comparative experiments, specifically regarding the effects of active pretraining on the performance of currently available benchmarks. You may also fine-tune and adapt Tucano models for deployment if your use follows the Apache 2.0 license. If you decide to use the Tucano models as a basis for your fine-tuned model, please conduct your own risk and bias assessment.

Out-of-scope Use

Tucano models are not intended for deployment. They are not an out-of-the-box product and should not be used for human-facing interactions.
Tucano models are for the Portuguese language only and are unsuitable for text generation tasks in other languages.
Tucano models have not been fine-tuned for downstream tasks.

Basic usage

Using the pipeline:

from transformers import pipeline

generator = pipeline("text-generation", model="TucanoBR/Tucano-1b1-Instruct")

completions  = generator("<instruction>Qual cidade é a capital do estado do Rio Grande do Sul?</instruction>", num_return_sequences=2, max_new_tokens=100)

for comp in completions:
  print(f"🤖 {comp['generated_text']}")

Using the AutoTokenizer and AutoModelForCausalLM:

from transformers import GenerationConfig, TextGenerationPipeline, AutoTokenizer, AutoModelForCausalLM
import torch

# Specify the model and tokenizer
model_id = "TucanoBR/Tucano-1b1-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Specify the generation parameters as you like
generation_config = GenerationConfig(
    **{
    "do_sample": True,
    "max_new_tokens": 2048,
    "renormalize_logits": True,
    "repetition_penalty": 1.2,
    "temperature": 0.3,
    "top_k": 30,
    "top_p": 0.3,
    "use_cache": True, 
  }
)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
generator = TextGenerationPipeline(model=model, task="text-generation", tokenizer=tokenizer, device=device)

# Generate text
prompt = "<instruction>Qual cidade é a capital do estado do Rio Grande do Sul?</instruction>"
completion = generator(prompt, generation_config=generation_config)
print(completion[0]['generated_text'])

Limitations

Like almost all other language models trained on large text datasets scraped from the web, the Tucano models show behavior that does not make them an out-of-the-box solution to many real-world applications, especially those requiring factual, reliable, and nontoxic text generation. Tucano models are all subject to the following:

Hallucinations: Tucano models can produce content that can be mistaken as true facts, but are misleading or entirely false, i.e., hallucination.
Biases and Toxicity: Tucano models inherit the social and historical stereotypes from the data used to train them. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.
Unreliable Code: Tucano models may produce incorrect code snippets and statements. These code generations should not be treated as suggestions or accurate solutions.
Language Limitations: Tucano models are primarily designed to interact with Portuguese. Other languages might challenge its comprehension, leading to potential misinterpretations or errors in response.
Repetition and Verbosity: Tucano models 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.

Hence, even though our models are released with a permissive license, we urge users to perform their risk analysis on them if they intend to use them for real-world applications.

Evaluations

To evaluate the Instruct versions of our models, we used AlpacaEval 2.0 with length-controlled win rates, a fast and relatively cheap evaluation method that is highly correlated with human preferences and evaluations of pairwise comparisons. To learn more about our evaluation read our documentation.

	Avg. Length	Wins	Base Wins	Total Matches	Length-Controlled Win Rate (%)	LC Std. Error
Llama-3.2-3B-Instruct	1609	257	548	805	21.06	0.075
Tucano-2b4-Instruct	1843	151	654	805	13.00	0.071
Tucano-1b1-Instruct	1667	124	681	805	8.80	0.083
Llama-3.2-1B-Instruct	1429	99	706	805	7.15	0.057
TeenyTinyLlama-460m-Chat	1333	28	777	805	2.84	0.059
Sabiá-7b	5011	1	804	805	0.076	0.0043
Gervásio-7b	5740	1	804	805	0.026	0.0016

Cite as 🤗

@misc{correa2024tucanoadvancingneuraltext,
      title={{Tucano: Advancing Neural Text Generation for Portuguese}}, 
      author={Corr{\^e}a, Nicholas Kluge and Sen, Aniket and Falk, Sophia and Fatimah, Shiza},
      year={2024},
      eprint={2411.07854},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.07854}, 
}

Aknowlegments

We gratefully acknowledge the granted access to the Marvin cluster hosted by University of Bonn along with the support provided by its High Performance Computing & Analytics Lab.