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README.md
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---
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license: apache-2.0
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language:
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- en
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- fr
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pipeline_tag: text-generation
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---
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![image/png](https://huggingface.co/datasets/malteos/images/resolve/main/occiglot.medium.png)
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# Occiglot-7B-FR-EN
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> A [polyglot](https://en.wikipedia.org/wiki/Multilingualism#In_individuals) language model for the [Occident](https://en.wikipedia.org/wiki/Occident).
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>
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**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/).
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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.
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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)
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This is the first release of an ongoing open research project for multilingual language models.
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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!**
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### Model details
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- **Continued-pretraining from:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
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- **Model type:** Causal decoder-only transformer language model
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- **Languages:** English, French, and code.
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
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- **Compute resources:** [HessianAI's 42](https://hessian.ai/)
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- **Contributors:** Manuel Brack, Patrick Schramowski, Pedro Ortiz, Malte Ostendorff, Fabio Barth, Georg Rehm, Kristian Kersting
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- **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)
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- **Contact:** [Discord](https://discord.gg/wUpvYs4XvM)
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### How to use
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You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
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set a seed for reproducibility:
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```python
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>>> from transformers import pipeline, set_seed
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>>> generator = pipeline('text-generation', model='occiglot/occiglot-7b-fr-en')
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>>> set_seed(42)
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>>> generator("Bonjour, Je suis un modèle linguistique,", max_length=40, num_return_sequences=1)
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[{'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'}]
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```
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## Dataset
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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.
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The data distribution by language (estimated) is as follows:
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- English: ~34%
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- Code: ~13%
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- French: ~52%
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The training data was prepared using [lm-datasets](https://github.com/malteos/lm-datasets).
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The exact data configuration is [here](https://huggingface.co/occiglot/occiglot-7b-eu5/blob/main/lm-datasets-config.yml).
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## Training settings
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- Continual pre-training on 128 x A100-80GB on [HessianAI's 42](https://hessian.ai/).
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- Framework: [Determined](https://www.determined.ai/)
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- Precision: bf16
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- Optimizer: AdamW (lr: 0.00001, warmup_steps: 420)
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- Global batch size: 512 (with 8192 blocksize) split over 128 GPUs
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- Cosine Annealing with Warmup
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## Tokenizer
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Tokenizer is unchanged from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
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## Evaluation
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Preliminary evaluation results can be found below.
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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.
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Currently, we are working on more suitable benchmarks for Spanish, French, German, and Italian.
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<details>
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<summary>Evaluation results</summary>
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### All 5 Languages
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| | avg | arc_challenge | belebele | hellaswag | mmlu | truthfulqa |
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|:---------------------------|---------:|----------------:|-----------:|------------:|---------:|-------------:|
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| Occiglot-7b-eu5 | 0.516895 | 0.508109 | 0.675556 | 0.718963 | 0.402064 | 0.279782 |
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| Occiglot-7b-eu5-instruct | 0.537799 | 0.53632 | 0.691111 | 0.731918 | 0.405198 | 0.32445 |
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| Occiglot-7b-fr-en | 0.509209 | 0.496806 | 0.691333 | 0.667475 | 0.409129 | 0.281303 |
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| Occiglot-7b-fr-en-instruct | 0.52884 | 0.515613 | 0.723333 | 0.67371 | 0.413024 | 0.318521 |
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| Claire-mistral-7b-0.1 | 0.514226 | 0.502773 | 0.705111 | 0.666871 | 0.412128 | 0.284245 |
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| Mistral-7b-v0.1 | 0.547111 | 0.528937 | 0.768444 | 0.682516 | 0.448253 | 0.307403 |
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| Mistral-7b-instruct-v0.2 | 0.56713 | 0.547228 | 0.741111 | 0.69455 | 0.422501 | 0.430262 |
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### English
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| | avg | arc_challenge | belebele | hellaswag | mmlu | truthfulqa |
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|:---------------------------|---------:|----------------:|-----------:|------------:|---------:|-------------:|
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| Occiglot-7b-eu5 | 0.59657 | 0.530717 | 0.726667 | 0.789882 | 0.531904 | 0.403678 |
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| Occiglot-7b-eu5-instruct | 0.617905 | 0.558874 | 0.746667 | 0.799841 | 0.535109 | 0.449 |
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| Occiglot-7b-fr-en | 0.621947 | 0.568259 | 0.771111 | 0.804919 | 0.570716 | 0.394726 |
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| Occiglot-7b-fr-en-instruct | 0.646571 | 0.586177 | 0.794444 | 0.808305 | 0.569862 | 0.474064 |
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| Claire-mistral-7b-0.1 | 0.651798 | 0.59727 | 0.817778 | 0.827126 | 0.600912 | 0.415906 |
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| Mistral-7b-v0.1 | 0.668385 | 0.612628 | 0.844444 | 0.834097 | 0.624555 | 0.426201 |
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| Mistral-7b-instruct-v0.2 | 0.713657 | 0.637372 | 0.824444 | 0.846345 | 0.59201 | 0.668116 |
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### French
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| | avg | arc_challenge_fr | belebele_fr | hellaswag_fr | mmlu_fr | truthfulqa_fr |
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|:---------------------------|---------:|-------------------:|--------------:|---------------:|----------:|----------------:|
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| Occiglot-7b-eu5 | 0.525017 | 0.506416 | 0.675556 | 0.712358 | 0.495684 | 0.23507 |
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| Occiglot-7b-eu5-instruct | 0.554216 | 0.541488 | 0.7 | 0.724245 | 0.499122 | 0.306226 |
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| Occiglot-7b-fr-en | 0.542903 | 0.532934 | 0.706667 | 0.718891 | 0.51333 | 0.242694 |
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| Occiglot-7b-fr-en-instruct | 0.567079 | 0.542344 | 0.752222 | 0.72553 | 0.52051 | 0.29479 |
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| Claire-mistral-7b-0.1 | 0.515127 | 0.486741 | 0.694444 | 0.642964 | 0.479566 | 0.271919 |
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| Mistral-7b-v0.1 | 0.558129 | 0.525235 | 0.776667 | 0.66481 | 0.543121 | 0.280813 |
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| Mistral-7b-instruct-v0.2 | 0.575821 | 0.551754 | 0.758889 | 0.67916 | 0.506837 | 0.382465 |
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</details>
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## Acknowledgements
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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)).
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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)
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through the project [OpenGPT-X](https://opengpt-x.de/en/) (project no. 68GX21007D).
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## License
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[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
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## See also
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- https://huggingface.co/collections/occiglot/occiglot-eu5-7b-v01-65dbed502a6348b052695e01
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