crodri/falcon_aguila7b_quantized

Ǎguila-7B

Table of Contents

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• Model description
• Intended uses and limitations
• How to use
• Limitations and bias
• Language adaptation
• Training
  • Training data
  • Training procedure
• Additional information
  • Author
  • Contact
  • Copyright
  • License
  • Funding
  • Disclaimer

Model description

Ǎguila-7B is a transformer-based causal language model for Catalan, Spanish, and English. It is based on the Falcon-7B model and has been trained on a 26B token trilingual corpus collected from publicly available corpora and crawlers. This is a quantized version using ct2-transformers-converter, as in [michaelfeil/ct2fast-falcon-7b] (https://huggingface.co/michaelfeil/ct2fast-falcon-7b)

Intended uses and limitations

The Ǎguila-7B model is ready-to-use only for causal language modeling to perform text-generation tasks. However, it is intended to be fine-tuned for downstream tasks.

How to use

Here is how to use this model:

from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer

model_name = "crodri/aguila_quantized"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = GeneratorCT2fromHfHub(
        # load in int8 on CUDA
        model_name_or_path=model_name,
        device="cuda",
        compute_type="int8_float16",
        # tokenizer=AutoTokenizer.from_pretrained("tiiuae/falcon-7b")
)
outputs = model.generate(
    text=["El millor de Barcelona es "],
    max_length=512,
    include_prompt_in_result=False
)
print(outputs)

Limitations and bias

At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.

Language adaptation

We adapted the original Falcon-7B model to Spanish and Catalan by swapping the tokenizer and adjusting the embedding layer.

The adaptation procedure is explained in this blog post.

Training

Training data

The training corpus consists of 26B tokens of several corpora gathered from web crawlings and public domain data.

Dataset	Language	Words (per-epoch)	Epochs
Wikipedia	en	2169.97M	1.428144485
C4_es	es	53709.80M	0.1049686196
Biomedical	es	455.03M	0.7140722425
Legal	es	995.70M	0.7140722425
Wikipedia	es	693.60M	1.428144485
Gutenberg	es	53.18M	0.7140722425
C4_ca	ca	2826.00M	2.142216727
Biomedical	ca	11.80M	1.428144485
RacoCatalà Noticias	ca	17.16M	2.142216727
RacoCatalà Forums	ca	333.73M	2.142216727
CaWaC	ca	57.79M	2.142216727
Wikipedia	ca	228.01M	3.570361212
Vilaweb	ca	50.34M	2.142216727

The dataset has the following language distribution:

Language	Percentage
En	16.84%
Es	41.38%
Ca	41.79%

Note: A small amount of English data was kept to avoid catastrophic forgetting.

Training procedure

The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) with a vocabulary size of 50,257 tokens. After training a new tokenizer and adapting falcon-7b's embedding layer, the model was further pre-trained in three target languages: Catalan, Spanish and English.

The training lasted a total of 320 hours on 8 NVIDIA H100 GPUs with 80GB RAM.

Training hyperparameters

• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• train_batch_size: 1
• eval_batch_size: 1
• total_train_batch_size: 8
• total_eval_batch_size: 8
• optimizer: Adam
• betas: (0.9,0.999)
• epsilon: 1e-08
• learning_rate: 5e-05
• lr_scheduler_type: linear
• num_epochs: 1.0

Framework versions

• Pytorch 2.0.0
• Transformers 4.30.2
• Datasets 2.13.1
• Tokenizers 0.13.3

Additional information

Author

The Language Technologies Unit from Barcelona Supercomputing Center.

Contact

For further information, please send an email to langtech@bsc.es.

Copyright

Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.

License

Apache License, Version 2.0

Funding

This work was funded by:

• The Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.
• The Spanish State Secretariat for Digitalization and Artificial Intelligence within the framework of the Plan de Impulso de las Tecnologías del Lenguaje.

Disclaimer

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The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0.

Be aware that the model may have biases and/or any other undesirable distortions.

When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

In no event shall the owner and creator of the model (Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties.