Ǎguila-7B
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and ongoing support, please visit https://huggingface.co/BSC-LT
Table of Contents
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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.
More information available in the following post from Medium.com: Introducing Ǎguila, a new open-source LLM for Spanish and Catalan (
https://medium.com/@mpamies247/introducing-a%CC%8Cguila-a-new-open-source-llm-for-spanish-and-catalan-ee1ebc70bc79)
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:
1import torch
2from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
3
4input_text = "El mercat del barri és fantàstic, hi pots trobar"
5
6model_id = "projecte-aina/aguila-7b"
7tokenizer = AutoTokenizer.from_pretrained(model_id)
8generator = pipeline(
9 "text-generation",
10 model=model_id,
11 tokenizer=tokenizer,
12 torch_dtype=torch.bfloat16,
13 trust_remote_code=True,
14 device_map="auto",
15)
16generation = generator(
17 input_text,
18 do_sample=True,
19 top_k=10,
20 eos_token_id=tokenizer.eos_token_id,
21)
22
23print(f"Result: {generation[0]['generated_text']}")
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
Funding
This work was funded by:
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.
