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occiglot-7b-eu5-instruct-hf

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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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+ - es
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+ - de
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+ - fr
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+ - it
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+ pipeline_tag: text-generation
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+ ---
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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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+
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+ # Occiglot-7B-EU5-Instruct
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+
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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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+
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+ **Occiglot-7B-EU5-Instruct** is a the instruct version of [occiglot-7b-eu5](https://huggingface.co/occiglot/occiglot-7b-eu5/), 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/).
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+ It was trained on 400M tokens of additional multilingual and code instructions.
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+ Note that the model was not safety aligned and might generate problematic outputs.
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+
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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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+
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+
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+ ### Model details
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+
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+ - **Instruction tuned from:** [occiglot-7b-eu5](https://huggingface.co/occiglot/occiglot-7b-eu5)
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+ - **Model type:** Causal decoder-only transformer language model
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+ - **Languages:** English, Spanish, French, German, Italian, 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:** [DFKI cluster](https://www.dfki.de/en/web)
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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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+
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+ ### How to use
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+
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+ The model was trained using the chatml instruction template. You can use the transformers chat template feature for interaction.
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+ Since the generation relies on some randomness, we
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+ set a seed for reproducibility:
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+
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+ ```python
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+ >>> from transformers import AutoTokenizer, MistralForCausalLM, set_seed
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+ >>> tokenizer = AutoTokenizer.from_pretrained("occiglot/occiglot-7b-eu5-instruct")
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+ >>> model = MistralForCausalLM.from_pretrained('occiglot/occiglot-7b-eu5-instruct') # You may want to use bfloat16 and/or move to GPU here
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+ >>> set_seed(42)
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+ >>> messages = [
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+ >>> {"role": "system", 'content': 'You are a helpful assistant. Please give short and concise answers.'},
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+ >>> {"role": "user", "content": "Wer ist der deutsche Bundeskanzler?"},
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+ >>> ]
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+ >>> tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_dict=False, return_tensors='pt',)
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+ >>> set_seed(42)
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+ >>> outputs = model.generate(tokenized_chat.to('cuda'), max_new_tokens=200,)
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+ >>> tokenizer.decode(out[0][len(tokenized_chat[0]):])
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+ 'Der deutsche Bundeskanzler ist Olaf Scholz.'
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+ ```
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+
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+ ## Dataset
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+
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+ The training data was split evenly amongst the 5 languages based on the total number of tokens. We would like to thank [Disco Research](https://huggingface.co/DiscoResearch), [Jan Philipp Harries](https://huggingface.co/jphme), and [Björn Plüster](https://huggingface.co/bjoernp) for making their dataset available to us.
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+
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+
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+ **English and Code**
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+ - [Open-Hermes-2B](https://huggingface.co/datasets/teknium/OpenHermes-2.5)
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+
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+ **German**
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+ - [DiscoLM German Dataset](https://huggingface.co/DiscoResearch) includes the publicly available [germanrag](https://huggingface.co/datasets/DiscoResearch/germanrag) dataset
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+ - [OASST-2](https://huggingface.co/datasets/OpenAssistant/oasst2) (German subset)
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+ - [Aya-Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) (German subset)
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+
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+ **Spanish**
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+ - [Mentor-ES](https://huggingface.co/datasets/projecte-aina/MentorES)
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+ - [Squad-es](https://huggingface.co/datasets/squad_es)
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+ - [OASST-2](https://huggingface.co/datasets/OpenAssistant/oasst2) (Spanish subset)
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+ - [Aya-Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) (Spanish subset)
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+
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+ **French**
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+ - [Bactrian-X](https://huggingface.co/datasets/MBZUAI/Bactrian-X) (French subset)
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+ - [AI-Society Translated](https://huggingface.co/datasets/camel-ai/ai_society_translated) (French subset)
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+ - [GT-Dorimiti](https://huggingface.co/datasets/Gt-Doremiti/gt-doremiti-instructions)
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+ - [OASST-2](https://huggingface.co/datasets/OpenAssistant/oasst2) (French subset)
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+ - [Aya-Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) (French subset)
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+
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+ **Italian**
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+ - [Quora-IT-Baize](https://huggingface.co/datasets/andreabac3/Quora-Italian-Fauno-Baize)
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+ - [Stackoverflow-IT-Vaize](https://huggingface.co/datasets/andreabac3/StackOverflow-Italian-Fauno-Baize)
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+ - [Camoscio](https://huggingface.co/datasets/teelinsan/camoscio_cleaned)
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+ - [OASST-2](https://huggingface.co/datasets/OpenAssistant/oasst2) (Italian subset)
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+ - [Aya-Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) (Italian subset)
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+
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+ ## Training settings
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+
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+ - Full instruction fine-tuning on 8xH100.
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+ - 0.6 - 4 training epochs (depending on dataset sampling).
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+ - Framework: [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
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+ - Precision: bf16
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+ - Optimizer: AdamW
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+ - Global batch size: 128 (with 8192 context length)
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+ - Cosine Annealing with Warmup
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+
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+
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+ ## Tokenizer
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+
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+ Tokenizer is unchanged from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
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+
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+ ## Evaluation
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+
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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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+
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+ <details>
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+ <summary>Evaluation results</summary>
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+
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+ ### All 5 Languages
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+
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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-de-en | 0.518337 | 0.496297 | 0.715111 | 0.669034 | 0.412545 | 0.298697 |
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+ | Occiglot-7b-de-en-instruct | 0.543173 | 0.530826 | 0.745778 | 0.67676 | 0.411326 | 0.351176 |
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+ | Occiglot-7b-it-en | 0.513221 | 0.500564 | 0.694444 | 0.668099 | 0.413528 | 0.289469 |
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+ | Occiglot-7b-it-en-instruct | 0.53721 | 0.523128 | 0.726667 | 0.683414 | 0.414918 | 0.337927 |
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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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+ | Occiglot-7b-es-en | 0.483388 | 0.482949 | 0.606889 | 0.653902 | 0.398922 | 0.274277 |
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+ | Occiglot-7b-es-en-instruct | 0.504023 | 0.494576 | 0.65 | 0.670847 | 0.406176 | 0.298513 |
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+ | Leo-mistral-hessianai-7b | 0.484806 | 0.462103 | 0.653556 | 0.642242 | 0.379208 | 0.28692 |
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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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+ | Lince-mistral-7b-it-es | 0.543427 | 0.540222 | 0.745111 | 0.692931 | 0.426241 | 0.312629 |
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+ | Cerbero-7b | 0.532385 | 0.513714 | 0.743111 | 0.654061 | 0.427566 | 0.323475 |
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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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+
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+
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+ ### English
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+
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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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+ | Leo-mistral-hessianai-7b | 0.600949 | 0.522184 | 0.736667 | 0.777833 | 0.538812 | 0.429248 |
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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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+
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+ ### German
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+
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+ | | avg | arc_challenge_de | belebele_de | hellaswag_de | mmlu_de | truthfulqa_de |
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+ |:---------------------------|---------:|-------------------:|--------------:|---------------:|----------:|----------------:|
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+ | Occiglot-7b-eu5 | 0.508311 | 0.493584 | 0.646667 | 0.666631 | 0.483406 | 0.251269 |
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+ | Occiglot-7b-eu5-instruct | 0.531506 | 0.529512 | 0.667778 | 0.685205 | 0.488234 | 0.286802 |
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+ | Occiglot-7b-de-en | 0.540085 | 0.50556 | 0.743333 | 0.67421 | 0.514633 | 0.26269 |
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+ | Occiglot-7b-de-en-instruct | 0.566474 | 0.54491 | 0.772222 | 0.688407 | 0.515915 | 0.310914 |
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+ | Leo-mistral-hessianai-7b | 0.517766 | 0.474765 | 0.691111 | 0.682109 | 0.488309 | 0.252538 |
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+ | Mistral-7b-v0.1 | 0.527957 | 0.476476 | 0.738889 | 0.610589 | 0.529567 | 0.284264 |
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+ | Mistral-7b-instruct-v0.2 | 0.535215 | 0.485885 | 0.688889 | 0.622438 | 0.501961 | 0.376904 |
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+
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+ ### Spanish
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+
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+ | | avg | arc_challenge_es | belebele_es | hellaswag_es | mmlu_es | truthfulqa_es |
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+ |:---------------------------|---------:|-------------------:|--------------:|---------------:|----------:|----------------:|
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+ | Occiglot-7b-eu5 | 0.533194 | 0.508547 | 0.676667 | 0.725411 | 0.499325 | 0.25602 |
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+ | Occiglot-7b-eu5-instruct | 0.548155 | 0.535043 | 0.68 | 0.737039 | 0.503525 | 0.285171 |
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+ | Occiglot-7b-es-en | 0.527264 | 0.529915 | 0.627778 | 0.72253 | 0.512749 | 0.243346 |
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+ | Occiglot-7b-es-en-instruct | 0.5396 | 0.545299 | 0.636667 | 0.734372 | 0.524374 | 0.257288 |
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+ | Lince-mistral-7b-it-es | 0.547212 | 0.52906 | 0.721111 | 0.687967 | 0.512749 | 0.285171 |
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+ | Mistral-7b-v0.1 | 0.554817 | 0.528205 | 0.747778 | 0.672712 | 0.544023 | 0.281369 |
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+ | Mistral-7b-instruct-v0.2 | 0.568575 | 0.54188 | 0.73 | 0.685406 | 0.511699 | 0.373891 |
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+
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+ ### French
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+
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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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+ | Cerbero-7b | 0.526044 | 0.462789 | 0.735556 | 0.624438 | 0.516462 | 0.290978 |
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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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+
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+ ### Italian
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+
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+ | | avg | arc_challenge_it | belebele_it | hellaswag_it | mmlu_it | truthfulqa_it |
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+ |:---------------------------|---------:|-------------------:|--------------:|---------------:|----------:|----------------:|
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+ | Occiglot-7b-eu5 | 0.421382 | 0.501283 | 0.652222 | 0.700533 | 0 | 0.252874 |
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+ | Occiglot-7b-eu5-instruct | 0.437214 | 0.516681 | 0.661111 | 0.71326 | 0 | 0.295019 |
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+ | Occiglot-7b-it-en | 0.432667 | 0.536356 | 0.684444 | 0.694768 | 0 | 0.247765 |
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+ | Occiglot-7b-it-en-instruct | 0.456261 | 0.545766 | 0.717778 | 0.713804 | 0 | 0.303959 |
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+ | Cerbero-7b | 0.434939 | 0.522669 | 0.717778 | 0.631567 | 0 | 0.302682 |
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+ | Mistral-7b-v0.1 | 0.426264 | 0.502139 | 0.734444 | 0.630371 | 0 | 0.264368 |
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+ | Mistral-7b-instruct-v0.2 | 0.442383 | 0.519247 | 0.703333 | 0.6394 | 0 | 0.349936 |
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+
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+
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+ </details>
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+
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+ ## Acknowledgements
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+
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+ 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)).
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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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+
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+
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+ ## License
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+
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+ [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
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+
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+ ## See also
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+
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+ - https://huggingface.co/collections/occiglot/occiglot-eu5-7b-v01-65dbed502a6348b052695e01
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+