--- 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.
Evaluation results ### 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 |
## 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