--- license: apache-2.0 language: - en - fr pipeline_tag: text-generation --- ![image/png](https://huggingface.co/datasets/malteos/images/resolve/main/occiglot.medium.png) # Occiglot-7B-FR-EN > A [polyglot](https://en.wikipedia.org/wiki/Multilingualism#In_individuals) language model for the [Occident](https://en.wikipedia.org/wiki/Occident). > **Occiglot-7B-FR-EN** is a generative language model with 7B parameters for French 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 113B 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-fr-en-instruct](https://huggingface.co/occiglot/occiglot-7b-fr-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, French, 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) [hello@occiglot.org](mailto:hello@occiglot.org) ### 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-fr-en') >>> set_seed(42) >>> generator("Bonjour, Je suis un modèle linguistique,", max_length=40, num_return_sequences=1) [{'generated_text': 'Bonjour, Je suis un modèle linguistique qui peut t'aider à traduire des textes entre le français et l'anglais. Si tu me donnes un texte en français'}] ``` ## 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. French plus English and Code. The data distribution by language (estimated) is as follows: - English: ~34% - Code: ~13% - French: ~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
## 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