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README.md

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