metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: WizardCoder
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 0.799
verified: false
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
🤗 HF Repo •🐱 Github Repo • 🐦 Twitter
📃 [WizardLM] • 📃 [WizardCoder] • 📃 [WizardMath]
👋 Join our Discord
News
[2023/01/04] 🔥 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.
[2023/01/04] 🔥 WizardCoder-33B-V1.1 outperforms ChatGPT 3.5, Gemini Pro, and DeepSeek-Coder-33B-instruct on HumanEval and HumanEval-Plus pass@1.
[2023/01/04] 🔥 WizardCoder-33B-V1.1 is comparable with ChatGPT 3.5, and surpasses Gemini Pro on MBPP and MBPP-Plus pass@1.
| Model | Checkpoint | Paper | HumanEval | HumanEval+ | MBPP | MBPP+ | License |
|---|---|---|---|---|---|---|---|
| GPT-4-Turbo (Nov 2023) | - | - | 85.4 | 81.7 | 83.0 | 70.7 | - |
| GPT-4 (May 2023) | - | - | 88.4 | 76.8 | - | - | - |
| GPT-3.5-Turbo (Nov 2023) | - | - | 72.6 | 65.9 | 81.7 | 69.4 | - |
| Gemini Pro | - | - | 63.4 | 55.5 | 72.9 | 57.9 | - |
| WizardCoder-33B-V1.1 | 🤗 HF Link | 📃 [WizardCoder] | 79.9 | 73.2 | 78.9 | 66.9 | Deepseek |
| WizardCoder-Python-34B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 73.2 | 64.6 | 73.2 | 59.9 | Llama2 |
| WizardCoder-15B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 59.8 | 52.4 | -- | -- | OpenRAIL-M |
| WizardCoder-Python-13B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 64.0 | -- | -- | -- | Llama2 |
| WizardCoder-Python-7B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 55.5 | -- | -- | -- | Llama2 |
| WizardCoder-3B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 34.8 | -- | -- | -- | OpenRAIL-M |
| WizardCoder-1B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 23.8 | -- | -- | -- | OpenRAIL-M |
❗ Data Contamination Check:
Before model training, we carefully and rigorously checked all the training data, and used multiple deduplication methods to verify and prevent data leakage on HumanEval and MBPP test set.
🔥 ❗Note for model system prompts usage:
Please use the same systems prompts strictly with us, and we do not guarantee the accuracy of the quantified versions.
Default version:
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
How to Reproduce the Performance of WizardCoder-33B-V1.1
We provide all codes here.
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}
}