--- license: apache-2.0 language: - en - es - de - fr - it pipeline_tag: text-generation --- ![image/png](https://huggingface.co/datasets/malteos/images/resolve/main/occiglot.medium.png) # Occiglot-7B-EU5 > A [polyglot](https://en.wikipedia.org/wiki/Multilingualism#In_individuals) language model for the [Occident](https://en.wikipedia.org/wiki/Occident). > **Occiglot-7B-EU5** is a generative language model with 7B parameters supporting the top-5 EU languages (English, Spanish, French, German, and Italian) 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 293B 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-eu5-instruct](https://huggingface.co/occiglot/occiglot-7b-eu5-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, French, German, Italian, 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-eu5') >>> set_seed(42) >>> generator("Hallo, Ich bin ein Sprachmodell,", max_length=40, num_return_sequences=1) [{'generated_text': 'Hallo, Ich bin ein Sprachmodell, das dir bei der Übersetzung von Texten zwischen Deutsch und Englisch helfen kann. Wenn du mir einen Text in Deutsch'}] ``` ## Dataset The training data was split amongst the 4 target languages (de, es, fr, it) and the continuous training in English and code. The data distribution by language (estimated) is as follows: - English: ~13% - Code: ~5% - German: ~20% - Spanish: ~20% - French: ~20% - Italian: ~20% 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 ### English | | arc_challenge | belebele | hellaswag | mmlu | truthfulqa | avg | |:-------------------------------------|----------------:|-----------:|------------:|---------:|-------------:|---------:| | occiglot/occiglot-7b-eu5 | 0.530717 | 0.726667 | 0.789882 | 0.531904 | 0.403678 | 0.59657 | | occiglot/occiglot-7b-eu5-instruct | 0.558874 | 0.746667 | 0.799841 | 0.535109 | 0.449034 | 0.617905 | | occiglot/occiglot-7b-de-en | 0.556314 | 0.791111 | 0.803824 | 0.568438 | 0.423251 | 0.628587 | | occiglot/occiglot-7b-de-en-instruct | 0.604096 | 0.812222 | 0.80004 | 0.570574 | 0.493807 | 0.656148 | | LeoLM/leo-mistral-hessianai-7b | 0.522184 | 0.736667 | 0.777833 | 0.538812 | 0.429248 | 0.600949 | | mistralai/Mistral-7B-v0.1 | 0.612628 | 0.844444 | 0.834097 | 0.624555 | 0.426201 | 0.668385 | | mistralai/Mistral-7B-Instruct-v0.2 | 0.637372 | 0.824444 | 0.846345 | 0.59201 | 0.668116 | 0.713657 | ### German | | arc_challenge_de | belebele_de | hellaswag_de | mmlu_de | truthfulqa_de | avg | |:-------------------------------------|-------------------:|--------------:|---------------:|----------:|----------------:|---------:| | occiglot/occiglot-7b-eu5 | 0.493584 | 0.646667 | 0.666631 | 0.483406 | 0.251269 | 0.508311 | | occiglot/occiglot-7b-eu5-instruct | 0.529512 | 0.667778 | 0.685205 | 0.488234 | 0.286802 | 0.531506 | | occiglot/occiglot-7b-de-en | 0.50556 | 0.743333 | 0.67421 | 0.514633 | 0.26269 | 0.540085 | | occiglot/occiglot-7b-de-en-instruct | 0.54491 | 0.772222 | 0.688407 | 0.515915 | 0.310914 | 0.566474 | | LeoLM/leo-mistral-hessianai-7b | 0.474765 | 0.691111 | 0.682109 | 0.488309 | 0.252538 | 0.517766 | | mistralai/Mistral-7B-v0.1 | 0.476476 | 0.738889 | 0.610589 | 0.529567 | 0.284264 | 0.527957 | | mistralai/Mistral-7B-Instruct-v0.2 | 0.485885 | 0.688889 | 0.622438 | 0.501961 | 0.376904 | 0.535215 | ### Spanish | | arc_challenge_es | belebele_es | hellaswag_es | mmlu_es | truthfulqa_es | avg | |:-------------------------------------|-------------------:|--------------:|---------------:|----------:|----------------:|---------:| | occiglot/occiglot-7b-eu5 | 0.508547 | 0.676667 | 0.725411 | 0.499325 | 0.25602 | 0.533194 | | occiglot/occiglot-7b-eu5-instruct | 0.535043 | 0.68 | 0.737039 | 0.503525 | 0.285171 | 0.548155 | | occiglot/occiglot-7b-es-en | 0.529915 | 0.627778 | 0.72253 | 0.512749 | 0.243346 | 0.527264 | | occiglot/occiglot-7b-es-en-instruct | 0.545299 | 0.636667 | 0.734372 | 0.524374 | 0.257288 | 0.5396 | | clibrain/lince-mistral-7b-it-es | 0.52906 | 0.721111 | 0.687967 | 0.512749 | 0.285171 | 0.547212 | | mistralai/Mistral-7B-v0.1 | 0.528205 | 0.747778 | 0.672712 | 0.544023 | 0.281369 | 0.554817 | | mistralai/Mistral-7B-Instruct-v0.2 | 0.54188 | 0.73 | 0.685406 | 0.511699 | 0.373891 | 0.568575 | ### French | | arc_challenge_fr | belebele_fr | hellaswag_fr | mmlu_fr | truthfulqa_fr | avg | |:-------------------------------------|-------------------:|--------------:|---------------:|----------:|----------------:|---------:| | occiglot/occiglot-7b-eu5 | 0.506416 | 0.675556 | 0.712358 | 0.495684 | 0.23507 | 0.525017 | | occiglot/occiglot-7b-eu5-instruct | 0.541488 | 0.7 | 0.724245 | 0.499122 | 0.306226 | 0.554216 | | occiglot/occiglot-7b-fr-en | 0.532934 | 0.706667 | 0.718891 | 0.51333 | 0.242694 | 0.542903 | | occiglot/occiglot-7b-fr-en-instruct | 0.542344 | 0.752222 | 0.72553 | 0.52051 | 0.29479 | 0.567079 | | OpenLLM-France/Claire-Mistral-7B-0.1 | 0.486741 | 0.694444 | 0.642964 | 0.479566 | 0.271919 | 0.515127 | | mistralai/Mistral-7B-v0.1 | 0.525235 | 0.776667 | 0.66481 | 0.543121 | 0.280813 | 0.558129 | | mistralai/Mistral-7B-Instruct-v0.2 | 0.551754 | 0.758889 | 0.67916 | 0.506837 | 0.382465 | 0.575821 | ### Italian | | arc_challenge_it | belebele_it | hellaswag_it | mmlu_it | truthfulqa_it | avg | |:-------------------------------------|-------------------:|--------------:|---------------:|----------:|----------------:|---------:| | Occiglot-7b-eu5 | 0.501283 | 0.652222 | 0.700533 | 0 | 0.252874 | 0.421382 | | Occiglot-7b-eu5-instruct | 0.516681 | 0.661111 | 0.71326 | 0 | 0.295019 | 0.437214 | | Occiglot-7b-it-en | 0.536356 | 0.684444 | 0.694768 | 0 | 0.247765 | 0.432667 | | Occiglot-7b-it-en-instruct | 0.545766 | 0.717778 | 0.713804 | 0 | 0.303959 | 0.456261 | | Cerbero-7b | 0.522669 | 0.717778 | 0.631567 | 0 | 0.302682 | 0.434939 | | Mistral-7B-v0.1 | 0.502139 | 0.734444 | 0.630371 | 0 | 0.264368 | 0.426264 | | Mistral-7B-Instruct-v0.2 | 0.519247 | 0.703333 | 0.6394 | 0 | 0.349936 | 0.442383 |
## 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/NikolayKozloff/occiglot-7b-eu5-GGUF - https://huggingface.co/collections/occiglot/occiglot-eu5-7b-v01-65dbed502a6348b052695e01