Papers › WizardCoder: Empowering Code Large Language Models with Evol-Instruct

WizardCoder: Empowering Code Large Language Models with Evol-Instruct

14 Jun 2023arXiv:2306.08568archive 2025-07-28

Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, QIngwei Lin, Daxin Jiang

Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on extensive raw code data without instruction fine-tuning. In this paper, we introduce WizardCoder, which empowers Code LLMs with complex instruction fine-tuning, by adapting the Evol-Instruct method to the domain of code. Through comprehensive experiments on four prominent code generation benchmarks, namely HumanEval, HumanEval+, MBPP, and DS-1000, we unveil the exceptional capabilities of our model. It surpasses all other open-source Code LLMs by a substantial margin. Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+. Our code, model weights, and data are public at https://github.com/nlpxucan/WizardLM

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2306.08568")

Code

Syntology Ran 3 of 7 code samples harvested from 2 repositories linked to this paper; 4 have no recorded run. Of those that ran: 2 ran · our draft was wrong; 1 ran with no contract checked.

By repository: community (archive-listed): 7 samples from 2 repositories, 3 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

nlpxucan/wizardlm officialmentioned in papermentioned on GitHubpytorch report
kyle-lyu/codeact mentioned on GitHubpytorch report
kyle-lyu/data-efficient-finetuning mentioned on GitHubpytorch report
nickrosh/evol-teacher mentioned on GitHubpytorchApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

7 samples harvested; 3 ran; 0 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

2ran · our draft was wrong
1ran
4unverified

Licence: 1 of the 7 samples is pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

construct_prompt kyle-lyu/data-efficient-finetuning/utils/dataset.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · fb57aad9f9c18c48 · report
extract_text nickrosh/evol-teacher/humaneval_gen.py community (archive-listed) ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · 7a733b18081436f3 · report
generate_prompt nickrosh/evol-teacher/humaneval_gen.py community (archive-listed) ran fingerprinted Apache-2.0 (permissive) · 13dc99a819a4f615 · report
check_instruction nickrosh/evol-teacher/generate_evol.py community (archive-listed) unverified Apache-2.0 (permissive) · 55673ce583151195 · report
convert_alpaca_to_evol nickrosh/evol-teacher/generate_evol.py community (archive-listed) unverified Apache-2.0 (permissive) · bb994e5a934af0d1 · report
get_model nickrosh/evol-teacher/humaneval_gen.py community (archive-listed) unverified Apache-2.0 (permissive) · c7517f1da7dd5864 · report
load_instructions nickrosh/evol-teacher/generate_evol.py community (archive-listed) unverified Apache-2.0 (permissive) · c618c35455396999 · report

Tasks

Code GenerationHumanEval

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Code Generation CodeContests WizardCoder-15B Test Set pass@1 1.11 #8 of 8 Archive leaderboard report
Code Generation CodeContests WizardCoder-15B Test Set pass@5 3.18 #8 of 8 Archive leaderboard report
Code Generation CodeContests WizardCoder-15B Val Set pass@1 1.98 #8 of 8 Archive leaderboard report
Code Generation CodeContests WizardCoder-15B Val Set pass@5 3.27 #8 of 8 Archive leaderboard report
Code Generation MBPP WizardCoder 15B Accuracy 51.8 #58 of 99 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections