llmchecks/CrawlIQ

WizardCoder: Empowering Code Large Language Models with Evol-Instruct

<p style="font-size:28px;" align="center"> 🏠 <a href="https://wizardlm.github.io/" target="blank">Home Page</a> </p> <p align="center"> <p align="center"> 🤗 <a href="https://huggingface.co/WizardLM" target="blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLMAI" target="blank">Twitter</a> </p> <p align="center"> 📃 <a href="https://arxiv.org/abs/2304.12244" target="blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="blank">[WizardMath]</a> <br> </p> <p align="center"> 👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a> </p>

News

[2024/01/04] 🔥 We released WizardCoder-33B-V1.1 trained from deepseek-coder-33b-base, the SOTA OSS Code LLM on EvalPlus Leaderboard, achieves 79.9 pass@1 on HumanEval, 73.2 pass@1 on HumanEval-Plus, 78.9 pass@1 on MBPP, and 66.9 pass@1 on MBPP-Plus.

[2024/01/04] 🔥 WizardCoder-33B-V1.1 outperforms ChatGPT 3.5, Gemini Pro, and DeepSeek-Coder-33B-instruct on HumanEval and HumanEval-Plus pass@1.

[2024/01/04] 🔥 WizardCoder-33B-V1.1 is comparable with ChatGPT 3.5, and surpasses Gemini Pro on MBPP and MBPP-Plus pass@1.

• —Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
• —Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the GSM8k Benchmarks, which is 24.8 points higher than the SOTA open-source LLM, and achieves 22.7 pass@1 on the MATH Benchmarks, which is 9.2 points higher than the SOTA open-source LLM.

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• —[08/09/2023] We released WizardLM-70B-V1.0 model. Here is Full Model Weight.

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Comparing WizardCoder-Python-34B-V1.0 with Other LLMs.

🔥 The following figure shows that our WizardCoder-Python-34B-V1.0 attains the second position in this benchmark, surpassing GPT4 (2023/03/15, 73.2 vs. 67.0), ChatGPT-3.5 (73.2 vs. 72.5) and Claude2 (73.2 vs. 71.2).

<p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/WizardCoder/imgs/compare_sota.png" alt="WizardCoder" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p>

Prompt Format

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

Inference Demo Script

We provide the inference demo code here.

Note: This script supports WizardLM/WizardCoder-Python-34B/13B/7B-V1.0. If you want to inference with WizardLM/WizardCoder-15B/3B/1B-V1.0, please change the stop_tokens = ['</s>'] to stop_tokens = ['<|endoftext|>'] in the script.

Citation

Please cite the repo if you use the data, method or code in this repo.

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