English(EN) I Trained a 757M-Parameter LLM From Scratch, Then Fine-Tuned Qwen Instead. Here's Everything I Learned.

开发者7.57亿参数LLM败给微调版Qwen模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-12 01:15

一位开发者记录了他们从头开始训练一个7.57亿参数LLM的经验，并将其与微调后的Qwen2.5-0.5B-Instruct模型进行了比较。开发者发现，尽管他们自己构建的模型规模更大，但其表现不如规模较小的预训练Qwen模型。文章详细介绍了技术过程，包括分词、位置嵌入、Transformer块和QLoRA微调技术，并强调了在数据准备和模型架构选择方面学到的经验。 AI

影响提供了关于从头开始训练LLM的挑战以及微调预训练模型的有效性的见解。

排序理由开发者关于训练和比较LLM的详细技术文章，包括方法论和经验教训。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

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本文如何被排名

Signal score: 57 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 开发者关于训练和比较LLM的详细技术文章，包括方法论和经验教训。[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, other; 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: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

dev.to — LLM tag TIER_1 English(EN) · Bhargav · 2026-11-12 01:15

我从头开始训练了一个7.57亿参数的大模型，然后微调了Qwen。我学到了所有东西。

Most "I built an LLM" posts show you a loss curve going down and stop there. This one is different, because my from-scratch model lost, and I think the why is the most useful thing I can teach you. Over the last few weeks I built two lan…

本文如何被排名

报道来源 [1]

我从头开始训练了一个7.57亿参数的大模型，然后微调了Qwen。我学到了所有东西。

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