RichardErkhov
/

projecte-aina_-_FLOR-760M-4bits

+Quantization made by Richard Erkhov.
+[Github](https://github.com/RichardErkhov)
+[Discord](https://discord.gg/pvy7H8DZMG)
+[Request more models](https://github.com/RichardErkhov/quant_request)
+FLOR-760M - bnb 4bits
+- Model creator: https://huggingface.co/projecte-aina/
+- Original model: https://huggingface.co/projecte-aina/FLOR-760M/
+Original model description:
+---
+language:
+- en
+- es
+- ca
+licence:
+- apache-2.0
+tags:
+- FLOR
+- bloom
+- spanish
+- catalan
+- english
+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
+---
+# FLOR-760M
+## 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)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [Additional information](#additional-information)
+</details>
+## Model description
+**FLOR-760M** is a 760M-parameter transformer-based causal language model for Catalan, Spanish, and English.
+It is the result of a language adaptation technique performed on [BLOOM-1.1B](https://huggingface.co/bigscience/bloom-1b1),
+which involves modifying the model's vocabulary and embedding layer and continuously pre-training the model with 26B tokens in our target languages.
+For more details, take a look at [this blogpost](https://medium.com/@mpamies247/flor-6-3b-a-chinchilla-compliant-model-for-catalan-spanish-and-english-7cdb389a9aac) about the project.
+## Intended uses and limitations
+The **FLOR-760M** model is ready-to-use only for causal language modeling.
+It can perform text-generation tasks and be fine-tuned for specific scenarios.
+## How to use
+```python
+import torch
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+input_text = "Sovint em trobo pensant en tot allò que"
+model_id  = "projecte-aina/FLOR-760M"
+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.
+## Training
+### Language adaptation and training
+The language adaptation technique used to create FLOR-760M requires the vocabulary of the source model
+to be adapted before continuing its pre-training with data in the target languages. Specifically, we proceeded as follows:
+1) We trained our own BPE tokenizer for Catalan, Spanish, and English, and replaced the original BLOOM tokenizer and vocabulary with it. This procedure implied a downsizing of the original BLOOM's embedding layer and, therefore, a model compression from 1.1B parameters to 760M.
+2) The embeddings corresponding to tokens that are present in both the original and the target vocabulary (matching tokens) were used for initialization.
+3) The embeddings from tokens not present in BLOOM's original vocabulary were initialized as the average of all embeddings.
+4) The model was initialized with the weights from BOOM-1.1B, and with our adapted tokenizer (step 1) and embeddings (steps 2-3).
+5) The model was then trained on a corpus that contains a mixture of Catalan, Spanish, and English data.
+### Training data
+The training corpus is the same that was used to train [Ǎguila-7B](https://huggingface.co/projecte-aina/aguila-7b).
+It 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 |
+### Languages
+The training data has the same amount of Catalan and Spanish texts, and a smaller amount of English data.
+The table below shows the final language distribution:
+|Language|Percentage|
+|--------|----------|
+|   English (EN)   |  16.84%  |
+|   Spanish (ES)   |  41.38%  |
+|   Catalan (CA)   |  41.79%  |
+### Training hyperparameters
+- seed: 1
+- distributed_type: [WSE-2](https://www.cerebras.net/product-chip/)
+- num_devices: 1
+- train_batch_size: 60
+- eval_batch_size:  60
+- optimizer: AdamW
+- betas: (0.9,0.95)
+- epsilon: 1e-08
+- weight_decay_rate: 0.1
+- learning_rate:
+  - scheduler: "Linear"
+    initial_learning_rate: 0.0
+    end_learning_rate: 4.1e-5
+    steps: 3050
+  - scheduler: "CosineDecay"
+    initial_learning_rate: 4.1e-5
+    end_learning_rate: 3.4e-6
+    steps: 209133
+  - scheduler: "Constant"
+    learning_rate: 2.2e-6
+- num_epochs: 1.0
+### Framework versions
+The training was conducted in a Cerebras' [CS-2 system](https://www.cerebras.net/product-system/)
+using the [cs-1.9.1](https://github.com/Cerebras/modelzoo/releases/tag/Release_1.9.1) release of their software.
+## Evaluation
+FLOR-760M has been evaluated on 5-shot, using EleutherAI's Evaluation Harness implementation, on several datasets in Catalan, Spanish, and English, with particular emphasis on Catalan datasets.
+The tasks were chosen to cover several evaluation areas in order to provide a comprehensive overview of the model's capabilities. The baselines used to compare our results are multilingual and English open-source 1.3B models: mGPT-1.3B, GPT-Neo-1.3B, Pythia-1.4B, OPT-1.3B, Falcon-rw-1.3B, and Cerebras-GPT-1.3B.
+Our implementation of EleutherAI's *LM Evaluation Harness* can be found [here](https://github.com/langtech-bsc/lm-evaluation-harness/tree/FLOR-eval).
+The following is a list of evaluation areas and their respective datasets:
+- Reading Comprehension: [Belebele](https://huggingface.co/datasets/facebook/belebele)
+- Question Answering: [XQuAD](https://huggingface.co/datasets/xquad), [CatalanQA](https://huggingface.co/datasets/projecte-aina/catalanqa), [CoQCat](https://huggingface.co/datasets/projecte-aina/CoQCat)
+- Natural Language Inference: [XNLI](https://huggingface.co/datasets/xnli) and its translation to Catalan ([XNLI-ca](https://huggingface.co/datasets/projecte-aina/xnli-ca)), [TE-ca](https://huggingface.co/datasets/projecte-aina/teca)
+- Paraphrase Identification: [PAWS-X](https://huggingface.co/datasets/paws-x) and its translation to Catalan ([PAWS-ca](https://huggingface.co/datasets/projecte-aina/PAWS-ca)), [Parafraseja](https://huggingface.co/datasets/projecte-aina/Parafraseja)
+- Commonsense Reasoning: [COPA](https://people.ict.usc.edu/~gordon/copa.html) and its translation to Catalan ([COPA-ca](https://huggingface.co/datasets/projecte-aina/COPA-ca))
+- Translation: [FLoRes](https://huggingface.co/datasets/flores)
+### Reading Comprehension and Questions Answering
+| Model | Belebele-ca | Belebele-es | Belebele-en | XQuAD-ca | XQuAD-es | XQuAD-en | CatalanQA | CoQCat |
+| ------|:-----------:|:-----------:|:-----------:|:--------:|:--------:|:--------:|:---------:|:------:|
+Random | 25.00 | 25.00 | 25.00 | - | - | - | - | - |
+mGPT-1.3B | 26.64 | 25.82 | 28.07 | 0.33 | 0.67 | 0.17 | 0.65 | 0.78 |
+GPT-Neo-1.3B | 39.55 | 37.50 | 42.83 | 19.75 | 29.77 | 51.53 | 22.34 | 23.57 |
+Pythia-1.4B | 38.32 | 36.89 | 44.26 | 26.19 | 34.13 | 52.98 | 27.47 | 25.38 |
+OPT-1.3B | 35.86 | 37.09 | 45.49 | 23.53 | 31.85 | 52.95 | 26.58 | 20.18 |
+Falcon-rw-1.3B | 34.84 | 35.66 | **50.61** | 5.93 | 19.25 | **58.60** | 6.91 | 15.61 |
+Cerebras-GPT-1.3B | 32.79 | 31.76 | 35.04 | 8.56 | 19.98 | 36.00 | 10.87 | 14.12 |
+BLOOM-1.1B | 39.34 | 38.32 | 41.19 | 36.81 | 36.98 | 44.10 | 44.65 | 34.57 |
+FLOR-760M | **41.19** | **39.55** | 36.68 | **41.10** | **41.11** | 40.20 | **51.01** | **41.34** |
+### Natural Language Inference and Paraphrase Identification
+| Model | XNLI-ca | XNLI-es | XNLI-en | TECA-ca | PAWS-X-ca | PAWS-X-es | PAWS-X-en | Parafraseja |
+| ------|:-------:|:-------:|:-------:|:-------:|:---------:|:---------:|:---------:|:-----------:|
+Random | 33.33 | 33.33 | 33.33 | 33.33 | 50.00 | 50.00 | 50.00 | 50.00 |
+mGPT-1.3B | 40.06 | 43.81 | 45.67 | 37.03 | 51.00 | 52.30 | 56.15 | 51.32 |
+GPT-Neo-1.3B | 41.44 | 45.57 | 49.92 | 35.38 | 54.65 | 53.40 | 54.60 | 51.70 |
+Pythia-1.4B | 42.46 | 45.61 | 51.00 | 37.46 | 54.15 | 52.50 | **57.70** | 55.23 |
+OPT-1.3B | 40.08 | 44.53 | **52.48** | 36.14 | 54.10 | 52.55 | 55.90 | 53.23 |
+Falcon-rw-1.3B | 34.53 | 35.85 | 45.73 | 34.96 | 54.25 | **54.05** | 53.65 | 50.60 |
+Cerebras-GPT-1.3B | 36.83 | 38.88 | 47.25 | 35.62 | 52.40 | 52.20 | 55.95 | 52.05 |
+BLOOM-1.1B | **47.19** | **46.39** | 49.44 | 41.38 | **55.05** | 54.05 | 54.75 | 55.65 |
+FLOR-760M | 46.93 | 46.03 | 46.11 | **42.14** | 52.35 | 52.50 | 54.85 | **56.55** |
+### Commonsense Reasoning and Translation
+| Model | XStoryCloze-es | XStoryCloze-en | COPA-ca | COPA-en | FloRes (ca->es) | FloRes (es->ca) | FloRes (ca->en) | FloRes (en->ca) | FloRes (es->en) | FloRes (en->es) |
+| ------|:--------------:|:--------------:|:-------:|:-------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
+Random | 50.00 | 50.00 | 50.00 | 50.00 | - | - | - | - | - | - |
+mGPT-1.3B | 55.33 | 60.09 | 52.20 | 63.40 | 3.25 | 2.96 | 9.25 | 3.79 | 17.75 | 15.34 |
+GPT-Neo-1.3B | 51.42 | 66.58 | 53.40 | 74.80 | 3.27 | 3.80 | 17.77 | 5.49 | 17.70 | 12.04 |
+Pythia-1.4B | 54.14 | 68.37 | 52.20 | 78.60 | 9.68 | 5.74 | 24.03 | 11.10 | 21.50 | 15.04 |
+OPT-1.3B | 53.94 | 69.95 | 52.60 | 76.20 | 3.14 | 3.52 | 15.39 | 2.00 | 16.33 | 6.53 |
+Falcon-rw-1.3B | 51.09 | **71.34** | 52.40 | **79.60** | 3.03 | 3.59 | 8.89 | 3.01 | 14.17 | 6.50 |
+Cerebras-GPT-1.3B | 49.11 | 60.62 | 51.40 | 66.80 | 2.42 | 1.81 | 2.69 | 0.82 | 3.36 | 1.77 |
+BLOOM-1.1B | 57.91 | 62.48 | 62.80 | 66.40 | 21.62 | 15.28 | 31.16 | 21.28 | **20.92** | 16.84 |
+FLOR-760M | **61.42** | 61.42 | **65.40** | 64.20 | **22.62** | **15.77** | **32.26** | **26.04** | 20.91 | **18.08** |
+## 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 [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).
+### 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>