WizardLM vs Codestral — Comparison

WizardLM

LLMs build upon Evol Insturct: WizardLM, WizardCoder, WizardMath - nlpxucan/WizardLM

Thanks to the enthusiastic friends, their video introductions are more lively and interesting. Please cite the paper if you use the data or code from WizardLM. Please cite the paper if you use the data or code from WizardCoder. Please cite the paper if you refer to our model or code or data or paper from WizardMath. ❗To commen concern about dataset: Recently, there have been clear changes in the open-source policy and regulations of our overall organization's code, data, and models. Despite this, we have still worked hard to obtain opening the weights of the model first, but the data involves stricter auditing and is in review with our legal team . Our researchers have no authority to publicly release them without authorization. Thank you for your understanding. 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. 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. 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. 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. 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. 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

Codestral

Empowering developers and democratising coding with Mistral AI.

Based on the provided content, there is insufficient meaningful user feedback to summarize opinions about Codestral. The social mentions consist mainly of unrelated spam posts, generic links, and simple YouTube video titles that just say "Codestral AI" without any actual reviews or user commentary. The only potentially relevant mention is a GitHub pricing update for vertex-ai that adds 70 new models, but this doesn't provide specific user sentiment about Codestral itself. More substantial user reviews and genuine social media discussions would be needed to provide an accurate assessment of user opinions about this software tool.

WizardLM

Codestral

WizardLM

Codestral

WizardLM

Codestral