English(EN) How I Ran 2,859 LLM Code Generation Tests with EvalScope — and Got Zero Errors

Qwen2.5-32B在2,859次LLM代码生成测试中实现零错误

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 07:07

一位开发者使用EvalScope框架对Qwen2.5-32B模型进行了细致的测试，运行了2,859个代码生成提示。这些测试涵盖了结构化JSON输出、函数调用和工具使用，令人惊讶的是结果为零错误。这种高可靠性，即使与云API相比，也表明其在需要强大顺序操作的自主代理应用方面具有巨大潜力。 AI

影响展示了Qwen2.5-32B的高可靠性，可能支持更强大的自主代理应用。

排序理由该集群详细介绍了对现有模型在特定任务上性能的严格评估，而不是新版本发布或行业重大转变。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群详细介绍了对现有模型在特定任务上性能的严格评估，而不是新版本发布或行业重大转变。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: model release, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 128 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

dev.to — LLM tag TIER_1 English(EN) · Storm Engine Technology. · 2026-06-02 07:07

我如何使用EvalScope运行2859次LLM代码生成测试——并且零错误

After three weeks of running Qwen2.5-32B on a DGX Spark, the number that surprised me most wasn't the throughput or latency. It was zero. Zero structural errors across 2,859 code generation tests. What I Tested EvalScope with code generation tasks covering…