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---
language:
- en
library_name: transformers
pipeline_tag: question-answering
license: apache-2.0
datasets:
- togethercomputer/RedPajama-Data-1T
- allenai/qasper
- DataHammer/paper_ground_dialog
metrics:
- bleu
- rouge
- f1
- bertscore
---
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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](https://github.com/gmftbyGMFTBY/science-llm) 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](https://github.com/gmftbyGMFTBY/science-llm/blob/main/LICENSE)
- **License:** English
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [Github Repo](https://github.com/gmftbyGMFTBY/science-llm)
- **Paper [optional]:** [Paper Repo]() |