69 lines
2.1 KiB
Markdown
69 lines
2.1 KiB
Markdown
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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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