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	---
	language:
	- en
	- fr
	license: apache-2.0
	tags:
	- exl2
	pipeline_tag: text-generation
	---

	![image/png](https://huggingface.co./datasets/malteos/images/resolve/main/occiglot.medium.png)

	# Occiglot-7B-FR-EN-Instruct

	> 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-Instruct is a the instruct version of [occiglot-7b-fr-en](https://huggingface.co./occiglot/occiglot-7b-fr-en), a generative language model with 7B parameters supporting the French and English and trained by the [Occiglot Research Collective](https://occiglot.github.io/occiglot/).
	It was trained on 160M tokens of additional multilingual and code instructions.
	Note that the model was not safety aligned and might generate problematic outputs.

	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

	- Instruction tuned from: [occiglot-7b-fr-en](https://huggingface.co./occiglot/occiglot-7b-fr-en)
	- 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: [DFKI cluster](https://www.dfki.de/en/web)
	- 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

	The model was trained using the chatml instruction template. You can use the transformers chat template feature for interaction.
	Since the generation relies on some randomness, we
	set a seed for reproducibility:

	```python
	>>> from transformers import AutoTokenizer, MistralForCausalLM, set_seed
	>>> tokenizer = AutoTokenizer.from_pretrained("occiglot/occiglot-7b-es-en-instruct")
	>>> model = MistralForCausalLM.from_pretrained('occiglot/occiglot-7b-es-en-instruct') # You may want to use bfloat16 and/or move to GPU here
	>>> set_seed(42)
	>>> messages = [
	>>> {"role": "system", 'content': 'You are a helpful assistant. Please give short and concise answers.'},
	>>> {"role": "user", "content": "qui est le président français ?"},
	>>> ]
	>>> tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_dict=False, return_tensors='pt',)
	>>> set_seed(42)
	>>> outputs = model.generate(tokenized_chat.to('cuda'), max_new_tokens=200,)
	>>> tokenizer.decode(out[0][len(tokenized_chat[0]):])
	'Le président français est Emmanuel Macron.'
	```

	## Dataset

	The training data was split evenly amongst French and English based on the total number of tokens.

	English and Code
	- [Open-Hermes-2B](https://huggingface.co./datasets/teknium/OpenHermes-2.5)


	French
	- [Bactrian-X](https://huggingface.co./datasets/MBZUAI/Bactrian-X) (French subset)
	- [AI-Society Translated](https://huggingface.co./datasets/camel-ai/ai_society_translated) (French subset)
	- [GT-Dorimiti](https://huggingface.co./datasets/Gt-Doremiti/gt-doremiti-instructions)
	- [OASST-2](https://huggingface.co./datasets/OpenAssistant/oasst2) (French subset)
	- [Aya-Dataset](https://huggingface.co./datasets/CohereForAI/aya_dataset) (French subset)

	## Training settings

	- Full instruction fine-tuning on 8xH100.
	- 0.6 - 4 training epochs (depending on dataset sampling).
	- Framework: [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
	- Precision: bf16
	- Optimizer: AdamW
	- Global batch size: 128 (with 8192 context length)
	- 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/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 \|

	### 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 \|

	</details>

	## Acknowledgements

	The pre-trained 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
	- https://huggingface.co./NikolayKozloff/occiglot-7b-fr-en-GGUF