YAML Metadata
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The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
Instruction-fine-tuned German language model (6B parameters; early alpha version)
Base model: malteos/bloom-6b4-clp-german (Ostendorff and Rehm, 2023)
Trained on:
- 20B additional German tokens (Wikimedia dumps and OSCAR 2023)
- OpenAssistant/oasst1 (German subset)
- LEL-A/translated_german_alpaca_validation
- LEL-A's version of deepset/germandpr
Chat demo
https://opengptx.dfki.de/chat/
Please note that this a research prototype and may not be suitable for extensive use.
How to cite
If you are using our code or models, please cite our paper:
@misc{Ostendorff2023clp,
doi = {10.48550/ARXIV.2301.09626},
author = {Ostendorff, Malte and Rehm, Georg},
title = {Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning},
publisher = {arXiv},
year = {2023}
}
License
Acknowledgements
This model was trained during the Helmholtz GPU Hackathon 2023. We gratefully thank the organizers for hosting this event and the provided computing resources.
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