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YAML Metadata Warning: 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, audio-text-to-text, 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, video-text-to-text, keypoint-detection, any-to-any, other

Model Details

Model Description

Mozi is the first large-scale language model for the scientific paper domain, such as question answering and emotional support. With the help of the large-scale language and evidence retrieval models, SciDPR, Mozi generates concise and accurate responses to users' questions about specific papers and provides emotional support for academic researchers.

Developed by: See GitHub repo for model developers
Model date: Mozi was trained In May. 2023.
Model version: This is version 1 of the model.
Model type: Mozi is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B parameters.
Language(s) (NLP): Apache 2.0
License: Chinese

Model Sources [optional]

Repository: Github Repo
Paper [optional]: Paper Repo

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