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--- |
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inference: false |
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language: |
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- en |
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tags: |
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- instruction-finetuning |
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pretty_name: JudgeLM-100K |
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task_categories: |
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- text-generation |
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--- |
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<br> |
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# JudgeLM Model Card |
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## Model Details |
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JudgeLM is a judge model trained by fine-tuning Vicuna on JudgeLM-100K dataset. |
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- **Developed by:** [HUST](https://english.hust.edu.cn/), [BAAI](https://www.baai.ac.cn/english.html) |
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- **Model type:** An auto-regressive language model based on the transformer architecture. |
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- **License:** Non-commercial license |
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- **Finetuned from model:** [Vicuna](https://vicuna.lmsys.org). |
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### Model Sources |
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- **Repository:** https://github.com/baaivision/JudgeLM |
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- **Paper:** https://arxiv.org/abs/2310.17631 |
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- **Demo:** http://218.91.113.230:9004/ |
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## Uses |
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The primary use of JudgeLM is research on evaluating the performance of large language models and chatbots. |
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The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. |
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## How to Get Started with the Model |
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- Judge large language models with this model: https://github.com/baaivision/JudgeLM/tree/main/judgelm/llm_judge. |
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- Serve this model with the gradio: https://github.com/baaivision/JudgeLM/tree/main/judgelm/serve. |
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## Training Details |
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JudgeLM v1.0 is fine-tuned from Vicuna-v1.3 with supervised instruction fine-tuning. |
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The training data is around 200K judge samples from [JudgeLM-100K dataset](https://huggingface.co/datasets/BAAI/JudgeLM-100K). |
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See more details in the "Fine-tuning Settings" section in the appendix of this [paper](https://arxiv.org/abs/2310.17631). |
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## Evaluation |
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JudgeLM is evaluated on JudgeLM val set, with judgements produced by GPT-4 teacher. See more details in this [paper](https://arxiv.org/abs/2310.17631) and try it with [code](https://github.com/baaivision/JudgeLM/tree/main/judgelm/llm_judge). |
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## Additional Information |
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### Citation Information |
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``` |
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@article{zhu2023judgelm, |
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title={JudgeLM: Fine-tuned Large Language Models are Scalable Judges}, |
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author={Lianghui Zhu and Xinggang Wang and Xinlong Wang}, |
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year={2023}, |
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eprint={2310.17631}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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