Beginner score
48/100
Competition workspace
适合已有大语言模型微调和代码数据处理经验的学生、工程师或个人开发者团队。参赛者需要能够围绕代码补全准备训练数据、训练 CodeLlama-7B，并提交可复现的数据处理脚本、数据集和源代码材料。
Suggested next step
Decide whether this contest fits your current stage before you sink time into the leaderboard.
Beginner score
48/100
Learning value
60/100
Estimated effort
12-40 hours
Metric
Official scoring is published on the Op…
适合已有大语言模型微调和代码数据处理经验的学生、工程师或个人开发者团队。参赛者需要能够围绕代码补全准备训练数据、训练 CodeLlama-7B，并提交可复现的数据处理脚本、数据集和源代码材料。
If the items below still feel unfamiliar, you usually get a better result by preparing first instead of rushing in.
CodeLlama-7B 微调
代码补全数据构建
训练数据清洗与处理
Python 与深度学习训练脚本
JSON 结果文件生成
模型训练可复现文档编写
The real friction is usually not library usage. It is validation, time allocation, and task framing.
核心难点是自行根据少量样例准备并优化代码补全训练数据，同时让微调效果可复现；仅提交预测结果不够，还需交付数据处理脚本、受限规模的数据集、原理说明和模型优化文档。初赛还要求将生成结果转换为指定格式的 JSON 文件，并可能被随机抽测复现。
This guide is the best pre-read if you want a cleaner start instead of trial-and-error.
How to learn feature engineering, validation, and competition workflow without heavy hardware.
No GPU? Pick Competitions That Still Teach You Good HabitsUse these fields to make a quick decision before you dive deeper.
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Official metric is not published on the OpenAtom competition listing; verify scoring on the competition page.
reward_value_usd is a rough CNY→USD estimate (×0.14) for ranking only; use reward_summary for the official prize text.