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---
license: apache-2.0
language:
- en
- es
pipeline_tag: text-generation
---

![image/png](https://huggingface.co/datasets/malteos/images/resolve/main/occiglot.medium.png)

# Occiglot-7B-ES-EN

> A [polyglot](https://en.wikipedia.org/wiki/Multilingualism#In_individuals) language model for the [Occident](https://en.wikipedia.org/wiki/Occident).
> 

**Occiglot-7B-ES-EN** is a generative language model with 7B parameters for Spanish and English and trained by the [Occiglot Research Collective](https://occiglot.github.io/occiglot/).
It is based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and trained on 112B tokens of additional multilingual and code data with a block size of 8,192 tokens per sample.
Note that the model is a general-purpose base model and was not instruction-fine-tuned nor optimized for chat or other applications. We make an instruction tuned variant available as [occiglot-7b-es-en-instruct](https://huggingface.co/occiglot/occiglot-7b-es-en-instruct)

This is the first release of an ongoing open research project for multilingual language models. 
If you want to train a model for your own language or are working on evaluations, please contact us or join our [Discord server](https://discord.gg/wUpvYs4XvM). **We are open for collaborations!**


### Model details

- **Continued-pretraining from:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Model type:** Causal decoder-only transformer language model
- **Languages:** English, Spanish, and code.
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
- **Compute resources:** [HessianAI's 42](https://hessian.ai/)
- **Contributors:** Manuel Brack, Patrick Schramowski, Pedro Ortiz, Malte Ostendorff, Fabio Barth, Georg Rehm, Kristian Kersting
- **Research labs:** [Occiglot](https://occiglot.github.io/occiglot/) with support from [SAINT](https://www.dfki.de/en/web/research/research-departments/foundations-of-systems-ai) and [SLT](https://www.dfki.de/en/web/research/research-departments/speech-and-language-technology)
- **Contact:** [Discord](https://discord.gg/wUpvYs4XvM)

### How to use

You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:

```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='occiglot/occiglot-7b-es-en')
>>> set_seed(42)
>>> generator("Hola, soy una modelo lingüística", max_length=40, num_return_sequences=1)
[{'generated_text': 'Hola, soy una modelo lingüística que puede ayudarte a traducir textos entre español e inglés. Si me envías un texto en español'}]
```

## Dataset

The training data is the respective subset of the data used for [occiglot-7b-eu5](https://huggingface.co/occiglot/occiglot-7b-eu5), i.e. Spanish plus English and Code.

The data distribution by language (estimated) is as follows:
- English: ~34%
- Code: ~13%
- Spanish: ~52%

The training data was prepared using [lm-datasets](https://github.com/malteos/lm-datasets). 
The exact data configuration is [here](https://huggingface.co/occiglot/occiglot-7b-eu5/blob/main/lm-datasets-config.yml).

## Training settings

- Continual pre-training on 128 x A100-80GB on [HessianAI's 42](https://hessian.ai/). 
- Framework: [Determined](https://www.determined.ai/)
- Precision: bf16
- Optimizer: AdamW (lr: 0.00001, warmup_steps: 420)
- Global batch size: 512 (with 8192 blocksize) split over 128 GPUs
- Cosine Annealing with Warmup


## Tokenizer

Tokenizer is unchanged from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).

## Evaluation

Preliminary evaluation results can be found below. 
Please note that the non-English results are based on partially machine-translated datasets and English prompts ([Belebele](https://huggingface.co/datasets/facebook/belebele) and [Okapi framework](https://github.com/nlp-uoregon/Okapi)) and thus should be interpreted with caution, e.g., biased towards English model performance.
Currently, we are working on more suitable benchmarks for Spanish, French, German, and Italian.

<details>
<summary>Evaluation results</summary>
  
### All 5 Languages

|                            |      avg |   arc_challenge |   belebele |   hellaswag |     mmlu |   truthfulqa |
|:---------------------------|---------:|----------------:|-----------:|------------:|---------:|-------------:|
| Occiglot-7b-eu5            | 0.516895 |        0.508109 |   0.675556 |    0.718963 | 0.402064 |     0.279782 |
| Occiglot-7b-eu5-instruct   | 0.537799 |        0.53632  |   0.691111 |    0.731918 | 0.405198 |     0.32445  |
| Occiglot-7b-es-en          | 0.483388 |        0.482949 |   0.606889 |    0.653902 | 0.398922 |     0.274277 |
| Occiglot-7b-es-en-instruct | 0.504023 |        0.494576 |   0.65     |    0.670847 | 0.406176 |     0.298513 |
| Lince-mistral-7b-it-es     | 0.543427 |        0.540222 |   0.745111 |    0.692931 | 0.426241 |     0.312629 |
| Mistral-7b-v0.1            | 0.547111 |        0.528937 |   0.768444 |    0.682516 | 0.448253 |     0.307403 |
| Mistral-7b-instruct-v0.2   | 0.56713  |        0.547228 |   0.741111 |    0.69455  | 0.422501 |     0.430262 |


### English

|                            |      avg |   arc_challenge |   belebele |   hellaswag |     mmlu |   truthfulqa |
|:---------------------------|---------:|----------------:|-----------:|------------:|---------:|-------------:|
| Occiglot-7b-eu5            | 0.59657  |        0.530717 |   0.726667 |    0.789882 | 0.531904 |     0.403678 |
| Occiglot-7b-eu5-instruct   | 0.617905 |        0.558874 |   0.746667 |    0.799841 | 0.535109 |     0.449    |
| Occiglot-7b-es-en          | 0.593609 |        0.543515 |   0.697778 |    0.788289 | 0.548355 |     0.390109 |
| Occiglot-7b-es-en-instruct | 0.615707 |        0.552048 |   0.736667 |    0.797451 | 0.557328 |     0.435042 |
| Leo-mistral-hessianai-7b   | 0.600949 |        0.522184 |   0.736667 |    0.777833 | 0.538812 |     0.429248 |
| Mistral-7b-v0.1            | 0.668385 |        0.612628 |   0.844444 |    0.834097 | 0.624555 |     0.426201 |
| Mistral-7b-instruct-v0.2   | 0.713657 |        0.637372 |   0.824444 |    0.846345 | 0.59201  |     0.668116 |

### Spanish

|                            |      avg |   arc_challenge_es |   belebele_es |   hellaswag_es |   mmlu_es |   truthfulqa_es |
|:---------------------------|---------:|-------------------:|--------------:|---------------:|----------:|----------------:|
| Occiglot-7b-eu5            | 0.533194 |           0.508547 |      0.676667 |       0.725411 |  0.499325 |        0.25602  |
| Occiglot-7b-eu5-instruct   | 0.548155 |           0.535043 |      0.68     |       0.737039 |  0.503525 |        0.285171 |
| Occiglot-7b-es-en          | 0.527264 |           0.529915 |      0.627778 |       0.72253  |  0.512749 |        0.243346 |
| Occiglot-7b-es-en-instruct | 0.5396   |           0.545299 |      0.636667 |       0.734372 |  0.524374 |        0.257288 |
| Lince-mistral-7b-it-es     | 0.547212 |           0.52906  |      0.721111 |       0.687967 |  0.512749 |        0.285171 |
| Mistral-7b-v0.1            | 0.554817 |           0.528205 |      0.747778 |       0.672712 |  0.544023 |        0.281369 |
| Mistral-7b-instruct-v0.2   | 0.568575 |           0.54188  |      0.73     |       0.685406 |  0.511699 |        0.373891 |



</details>

## Acknowledgements

The model training was supported by a compute grant at the [42 supercomputer](https://hessian.ai/)  which is a central component in the development of [hessian AI](https://hessian.ai/), the [AI Innovation Lab](https://hessian.ai/infrastructure/ai-innovationlab/) (funded by the [Hessian Ministry of Higher Education, Research and the Art (HMWK)](https://wissenschaft.hessen.de) & the [Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)](https://innen.hessen.de)) and the [AI Service Centers](https://hessian.ai/infrastructure/ai-service-centre/) (funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)).
The curation of the training data is partially funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)
through the project [OpenGPT-X](https://opengpt-x.de/en/) (project no. 68GX21007D).


## License

[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)

## See also

- https://huggingface.co/collections/occiglot/occiglot-eu5-7b-v01-65dbed502a6348b052695e01