Initial upload

quantized version of foundational model

README.md

@@ -1,3 +1,245 @@
 ---
-license: mit
+language:
+- en
+- es
+- ca
+licence:
+- apache-2.0
+tags:
+- aguila
+- falcon
+- spanish
+- catalan
+metrics:
+- ppl
+model-index:
+- name: aguila_7b
+  results:
+  - task:
+      name: Causal Language Modeling
+      type: text-generation
+    metrics:
+    - name: Perplexity
+      type: ppl
+      value: 8.59
+pipeline_tag: text-generation
+widget:
+- text: |-
+    Respon a la pregunta següent.
+    Pregunta: "Quina és la capital de Suècia?"
+    Resposta: "La capital de Suècia és Estocolm."
+    ----
+    Respon a la pregunta següent.
+    Pregunta: "Quina beguda es consumeix als matins per despertar-se?"
+    Resposta: "La majoria de gent consumeix cafè per despertar-se."
+    ----
+    Respon a la pregunta següent.
+    Pregunta: "Explica com funciona un motor de combustió"
+    Resposta:
+  example_title: Pregunta-Resposta
+- text: |-
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Me llamo Wolfgang y vivo en Berlin"
+    Entidades: Wolfgang:PER, Berlin:LOC
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center"
+    Entidades: parc güell:LOC, barcelona supercomputing center:LOC
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Maria y Miguel no tienen ningún problema contigo"
+    Entidades: Maria:PER, Miguel:PER
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Damián se cortó el pelo"
+    Entidades: Damián:PER
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Lo mejor de Barcelona és el bar de mi amigo Pablo"
+    Entidades: Pablo:PER, Barcelona:LOC
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Carlos comparte piso con Marc"
+    Entidades:
+  example_title: Entidades-Nombradas
 ---
+
+# Ǎguila-7B
+
+## Table of Contents
+<details>
+<summary>Click to expand</summary>
+
+- [Model description](#model-description)
+- [Intended uses and limitations](#intended-uses-and-limitations)
+- [How to use](#how-to-use)
+- [Limitations and bias](#limitations-and-bias)
+- [Language adaptation](#language-adaptation)
+- [Training](#training)
+  - [Training data](#training-data)
+  - [Training procedure](#training-procedure)
+- [Additional information](#additional-information)
+  - [Author](#author)
+  - [Contact](#contact)
+  - [Copyright](#copyright)
+  - [License](#license)
+  - [Funding](#funding)
+  - [Disclaimer](#disclaimer)
+
+</details>
+
+## Model description
+
+**Ǎguila-7B** is a transformer-based causal language model for Catalan, Spanish, and English. 
+It is based on the [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) model and has been trained on a 26B token 
+trilingual corpus collected from publicly available corpora and crawlers.
+
+
+## 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:
+
+```python
+import torch
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+
+input_text = "El mercat del barri és fantàstic, hi pots trobar"
+
+model_id  = "projecte-aina/aguila-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+generator = pipeline(
+    "text-generation",
+    model=model_id,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+generation = generator(
+    input_text,
+    do_sample=True,
+    top_k=10,
+    eos_token_id=tokenizer.eos_token_id,
+)
+
+print(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](https://huggingface.co/tiiuae/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](https://medium.com/@mpamies247/ee1ebc70bc79).
+
+## 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)](https://github.com/openai/gpt-2) with a vocabulary size of 50,257 tokens. 
+After training a new tokenizer and adapting [falcon-7b](https://huggingface.co/tiiuae/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](https://www.apache.org/licenses/LICENSE-2.0)
+
+### Funding
+This work was funded by:
+  - The [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
+  - The [Spanish State Secretariat for Digitalization and Artificial Intelligence](https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx) within the framework of the [Plan de Impulso de las Tecnologías del Lenguaje](https://plantl.mineco.gob.es/Paginas/index.aspx).
+
+### Disclaimer
+
+<details>
+<summary>Click to expand</summary>
+
+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.
+
+</details>

config.json

@@ -0,0 +1,6 @@
+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "layer_norm_epsilon": null,
+  "unk_token": "<|endoftext|>"
+}

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|endoftext|>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,33 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": true,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 2048,
+  "pad_token": null,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}