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---
license: apache-2.0
language:
- en
- it
pipeline_tag: text-generation
---
# Occiglot-7B-IT-EN
> A [polyglot](https://en.wikipedia.org/wiki/Multilingualism#In_individuals) language model for the [Occident](https://en.wikipedia.org/wiki/Occident).
>
**Occiglot-7B-IT-EN** is a generative language model with 7B parameters for Italian 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-it-en-instruct](https://huggingface.co/occiglot/occiglot-7b-it-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, 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-it-en')
>>> set_seed(42)
>>> generator("Salve, sono una modella linguistica,", max_length=40, num_return_sequences=1)
[{'generated_text': 'Salve, sono una modella linguistica che può aiutarvi a tradurre testi tra l'italiano e l'inglese. Se mi inviate un testo in italiano'}]
```
## 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. Italian plus English and Code.
The data distribution by language (estimated) is as follows:
- English: ~34%
- Code: ~13%
- Italian: ~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>
### English
| | arc_challenge | belebele | hellaswag | mmlu | truthfulqa | avg |
|:-------------------------------------|----------------:|-----------:|------------:|---------:|-------------:|---------:|
| Occiglot-7b-eu5 | 0.530717 | 0.726667 | 0.789882 | 0.531904 | 0.403678 | 0.59657 |
| Occiglot-7b-eu5-instruct | 0.558874 | 0.746667 | 0.799841 | 0.535109 | 0.449034 | 0.617905 |
| Occiglot-7b-it-en | 0.580205 | 0.774444 | 0.804222 | 0.578977 | 0.412786 | 0.630127 |
| Occiglot-7b-it-en-instruct | 0.609215 | 0.82 | 0.809301 | 0.578835 | 0.479562 | 0.659383 |
| Cerbero-7b | 0.613481 | 0.827778 | 0.810396 | 0.600484 | 0.480911 | 0.66661 |
| Mistral-7B-v0.1 | 0.612628 | 0.844444 | 0.834097 | 0.624555 | 0.426201 | 0.668385 |
| Mistral-7B-Instruct-v0.2 | 0.637372 | 0.824444 | 0.846345 | 0.59201 | 0.668116 | 0.713657 |
### 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 |
</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
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