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occiglot-7b-eu5

Text Generation Transformers Safetensors 5 languages mistral Inference Endpoints text-generation-inference
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Occiglot-7B-EU5

A polyglot language model for the Occident.

Occiglot-7B-EU5 is 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. It is based on Mistral-7B-v0.1 and trained on 293B 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-eu5-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. We are open for collaborations!

Model details

  • Continued-pretraining from: Mistral-7B-v0.1
  • Model type: Causal decoder-only transformer language model
  • Languages: English, Spanish, French, German, Italian, and code.
  • License: Apache 2.0
  • Compute resources: HessianAI's 42
  • Contributors: Manuel Brack, Patrick Schramowski, Pedro Ortiz, Malte Ostendorff, Fabio Barth, Georg Rehm, Kristian Kersting
  • Research labs: Occiglot with support from SAINT and SLT
  • Contact: Discord

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:

>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='occiglot/occiglot-7b-eu5')
>>> set_seed(42)
>>> generator("Hallo, Ich bin ein Sprachmodell,", max_length=40, num_return_sequences=1)
[{'generated_text': 'Hallo, Ich bin ein Sprachmodell, das dir bei der Übersetzung von Texten zwischen Deutsch und Englisch helfen kann. Wenn du mir einen Text in Deutsch'}]

Dataset

The training data was split amongst the 4 target languages (de, es, fr, it) and the continuous training in English and code.

The data distribution by language (estimated) is as follows:

  • English: ~13%
  • Code: ~5%
  • German: ~20%
  • Spanish: ~20%
  • French: ~20%
  • Italian: ~20%

The training data was prepared using lm-datasets. The exact data configuration is here.

Training settings

  • Continual pre-training on 128 x A100-80GB on HessianAI's 42.
  • Framework: Determined
  • 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.

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 and Okapi framework) 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

All 5 Languages

avg arc_challenge belebele hellaswag mmlu truthfulqa
Occiglot-7b-eu5 0.516895 0.508109 0.675556 0.718963 0.402064 0.279782
Occiglot-7b-eu5-instruct 0.537799 0.53632 0.691111 0.731918 0.405198 0.32445
Occiglot-7b-de-en 0.518337 0.496297 0.715111 0.669034 0.412545 0.298697
Occiglot-7b-de-en-instruct 0.543173 0.530826 0.745778 0.67676 0.411326 0.351176
Occiglot-7b-it-en 0.513221 0.500564 0.694444 0.668099 0.413528 0.289469
Occiglot-7b-it-en-instruct 0.53721 0.523128 0.726667 0.683414 0.414918 0.337927
Occiglot-7b-fr-en 0.509209 0.496806 0.691333 0.667475 0.409129 0.281303
Occiglot-7b-fr-en-instruct 0.52884 0.515613 0.723333 0.67371 0.413024 0.318521
Occiglot-7b-es-en 0.483388 0.482949 0.606889 0.653902 0.398922 0.274277
Occiglot-7b-es-en-instruct 0.504023 0.494576 0.65 0.670847 0.406176 0.298513
Leo-mistral-hessianai-7b 0.484806 0.462103 0.653556 0.642242 0.379208 0.28692
Claire-mistral-7b-0.1 0.514226 0.502773 0.705111 0.666871 0.412128 0.284245
Lince-mistral-7b-it-es 0.543427 0.540222 0.745111 0.692931 0.426241 0.312629
Cerbero-7b 0.532385 0.513714 0.743111 0.654061 0.427566 0.323475
Mistral-7b-v0.1 0.547111 0.528937 0.768444 0.682516 0.448253 0.307403
Mistral-7b-instruct-v0.2 0.56713 0.547228 0.741111 0.69455 0.422501 0.430262

English

avg arc_challenge belebele hellaswag mmlu truthfulqa
Occiglot-7b-eu5 0.59657 0.530717 0.726667 0.789882 0.531904 0.403678
Occiglot-7b-eu5-instruct 0.617905 0.558874 0.746667 0.799841 0.535109 0.449
Leo-mistral-hessianai-7b 0.600949 0.522184 0.736667 0.777833 0.538812 0.429248
Mistral-7b-v0.1 0.668385 0.612628 0.844444 0.834097 0.624555 0.426201
Mistral-7b-instruct-v0.2 0.713657 0.637372 0.824444 0.846345 0.59201 0.668116

German

avg arc_challenge_de belebele_de hellaswag_de mmlu_de truthfulqa_de
Occiglot-7b-eu5 0.508311 0.493584 0.646667 0.666631 0.483406 0.251269
Occiglot-7b-eu5-instruct 0.531506 0.529512 0.667778 0.685205 0.488234 0.286802
Occiglot-7b-de-en 0.540085 0.50556 0.743333 0.67421 0.514633 0.26269
Occiglot-7b-de-en-instruct 0.566474 0.54491 0.772222 0.688407 0.515915 0.310914
Leo-mistral-hessianai-7b 0.517766 0.474765 0.691111 0.682109 0.488309 0.252538
Mistral-7b-v0.1 0.527957 0.476476 0.738889 0.610589 0.529567 0.284264
Mistral-7b-instruct-v0.2 0.535215 0.485885 0.688889 0.622438 0.501961 0.376904

Spanish

avg arc_challenge_es belebele_es hellaswag_es mmlu_es truthfulqa_es
Occiglot-7b-eu5 0.533194 0.508547 0.676667 0.725411 0.499325 0.25602
Occiglot-7b-eu5-instruct 0.548155 0.535043 0.68 0.737039 0.503525 0.285171
Occiglot-7b-es-en 0.527264 0.529915 0.627778 0.72253 0.512749 0.243346
Occiglot-7b-es-en-instruct 0.5396 0.545299 0.636667 0.734372 0.524374 0.257288
Lince-mistral-7b-it-es 0.547212 0.52906 0.721111 0.687967 0.512749 0.285171
Mistral-7b-v0.1 0.554817 0.528205 0.747778 0.672712 0.544023 0.281369
Mistral-7b-instruct-v0.2 0.568575 0.54188 0.73 0.685406 0.511699 0.373891

French

avg arc_challenge_fr belebele_fr hellaswag_fr mmlu_fr truthfulqa_fr
Occiglot-7b-eu5 0.525017 0.506416 0.675556 0.712358 0.495684 0.23507
Occiglot-7b-eu5-instruct 0.554216 0.541488 0.7 0.724245 0.499122 0.306226
Occiglot-7b-fr-en 0.542903 0.532934 0.706667 0.718891 0.51333 0.242694
Occiglot-7b-fr-en-instruct 0.567079 0.542344 0.752222 0.72553 0.52051 0.29479
Claire-mistral-7b-0.1 0.515127 0.486741 0.694444 0.642964 0.479566 0.271919
Cerbero-7b 0.526044 0.462789 0.735556 0.624438 0.516462 0.290978
Mistral-7b-v0.1 0.558129 0.525235 0.776667 0.66481 0.543121 0.280813
Mistral-7b-instruct-v0.2 0.575821 0.551754 0.758889 0.67916 0.506837 0.382465

Italian

avg arc_challenge_it belebele_it hellaswag_it mmlu_it truthfulqa_it
Occiglot-7b-eu5 0.421382 0.501283 0.652222 0.700533 0 0.252874
Occiglot-7b-eu5-instruct 0.437214 0.516681 0.661111 0.71326 0 0.295019
Occiglot-7b-it-en 0.432667 0.536356 0.684444 0.694768 0 0.247765
Occiglot-7b-it-en-instruct 0.456261 0.545766 0.717778 0.713804 0 0.303959
Cerbero-7b 0.434939 0.522669 0.717778 0.631567 0 0.302682
Mistral-7b-v0.1 0.426264 0.502139 0.734444 0.630371 0 0.264368
Mistral-7b-instruct-v0.2 0.442383 0.519247 0.703333 0.6394 0 0.349936

Acknowledgements

The model training was supported by a compute grant at the 42 supercomputer which is a central component in the development of hessian AI, the AI Innovation Lab (funded by the Hessian Ministry of Higher Education, Research and the Art (HMWK) & the Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)) and the AI Service Centers (funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK)). The curation of the training data is partially funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the project OpenGPT-X (project no. 68GX21007D).

License

Apache 2.0

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