/WizardLM

Family of instruction-following LLMs powered by Evol-Instruct: WizardLM, WizardCoder

Primary LanguagePython

WizardLM: An Instruction-following LLM Using Evol-Instruct

Empowering Large Pre-Trained Language Models to Follow Complex Instructions

WizardLM

Code License Data License Python 3.9+

News

At present, our core contributors are preparing the 65B version and we expect to empower WizardLM with the ability to perform instruction evolution itself, aiming to evolve your specific data at a low cost.

Note for 30B and 13B model usage:

To obtain results identical to our demo, please strictly follow the prompts and invocation methods provided in the "src/infer_wizardlm13b.py" to use our 13B model for inference. Unlike the 7B model, the 13B model adopts the prompt format from Vicuna and supports multi-turn conversation.

For WizardLM-13B-V1.0, WizardLM-30B-V1.0 , the Prompt should be as following:

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: hello, who are you? ASSISTANT:

For WizardLM-7B-V1.0 , the Prompt should be as following:

"{instruction}\n\n### Response:"

For WizardCoder-15B-V1.0 , the Prompt should be as following:

"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"

GPT-4 automatic evaluation

We adopt the automatic evaluation framework based on GPT-4 proposed by FastChat to assess the performance of chatbot models. As shown in the following figure, WizardLM-30B achieved better results than Guanaco-65B.

WizardLM

WizardLM-30B performance on different skills.

The following figure compares WizardLM-30B and ChatGPT’s skill on Evol-Instruct testset. The result indicates that WizardLM-30B achieves 97.8% of ChatGPT’s performance on average, with almost 100% (or more than) capacity on 18 skills, and more than 90% capacity on 24 skills.

WizardLM

WizardLM performance on NLP foundation tasks.

The following table provides a comparison of WizardLMs and other LLMs on NLP foundation tasks. The results indicate that WizardLMs consistently exhibit superior performance in comparison to the LLaMa models of the same size. Furthermore, our WizardLM-30B model showcases comparable performance to OpenAI's Text-davinci-003 on the MMLU and HellaSwag benchmarks.

Model MMLU 5-shot ARC 25-shot TruthfulQA 0-shot HellaSwag 10-shot Average
Text-davinci-003 56.9 85.2 59.3 82.2 70.9
Vicuna-13b 1.1 51.3 53.0 51.8 80.1 59.1
Guanaco 30B 57.6 63.7 50.7 85.1 64.3
WizardLM-7B 1.0 42.7 51.6 44.7 77.7 54.2
WizardLM-13B 1.0 52.3 57.2 50.5 81.0 60.2
WizardLM-30B 1.0 58.8 62.5 52.4 83.3 64.2

WizardLM performance on code generation.

The following table provides a comprehensive comparison of WizardLMs and several other LLMs on the code generation task, namely HumanEval. The evaluation metric is pass@1. The results indicate that WizardLMs consistently exhibit superior performance in comparison to the LLaMa models of the same size. Furthermore, our WizardLM-30B model surpasses StarCoder and OpenAI's code-cushman-001. Moreover, our Code LLM, WizardCoder, demonstrates exceptional performance, achieving a pass@1 score of 57.3, surpassing the open-source SOTA by approximately 20 points.

Model HumanEval Pass@1
LLaMA-7B 10.5
LLaMA-13B 15.8
CodeGen-16B-Multi 18.3
CodeGeeX 22.9
LLaMA-33B 21.7
LLaMA-65B 23.7
PaLM-540B 26.2
CodeGen-16B-Mono 29.3
code-cushman-001 33.5
StarCoder 33.6
WizardLM-7B 1.0 19.1
WizardLM-13B 1.0 24.0
WizardLM-30B 1.0 37.8
WizardCoder-15B 1.0 57.3

Call for Feedbacks

We welcome everyone to use your professional and difficult instructions to evaluate WizardLM, and show us examples of poor performance and your suggestions in the issue discussion area. We are focusing on improving the Evol-Instruct now and hope to relieve existing weaknesses and issues in the the next version of WizardLM. After that, we will open the code and pipeline of up-to-date Evol-Instruct algorithm and work with you together to improve it.

Unofficial Video Introductions

Thanks to the enthusiastic friends, their video introductions are more lively and interesting.

  1. GET WizardLM NOW! 7B LLM KING That Can Beat ChatGPT! I'm IMPRESSED!
  2. WizardLM: Enhancing Large Language Models to Follow Complex Instructions
  3. WizardCoder AI Is The NEW ChatGPT's Coding TWIN!

Overview of Evol-Instruct

Evol-Instruct is a novel method using LLMs instead of humans to automatically mass-produce open-domain instructions of various difficulty levels and skills range, to improve the performance of LLMs.

WizardLM

WizardLM

Citation

Please cite the paper if you use the data or code from WizardLM.

@misc{xu2023wizardlm,
      title={WizardLM: Empowering Large Language Models to Follow Complex Instructions}, 
      author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
      year={2023},
      eprint={2304.12244},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Please cite the paper if you use the data or code from WizardCoder.

@misc{luo2023wizardcoder,
      title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, 
      author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang},
      year={2023},
      eprint={2306.08568},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Disclaimer

The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of WizardLM is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.