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English(EN) Training a 97.5M Python coding model from scratch for $1.40 in GPU time

开发者从头开始训练一个9750万参数的Python编码模型，成本仅为1.40美元

作者 PulseAugur 编辑部 · [1 个来源] ·

一位开发者详细介绍了从头开始训练一个名为Keera的小型、9750万参数的Python编码语言模型的过程，GPU时间成本约为1.40美元。该模型模仿了GPT-2 small的架构，在经过微调后，在HumanEval基准测试中达到了5.5%的成功率。该项目涉及创建自定义分词器、训练循环，并利用RoPE进行位置编码和RMSNorm进行激活归一化等特定架构选择，以优化性能和成本。 AI

影响 展示了训练小型、专业化语言模型以完成编码等特定任务的可行性和成本效益。

排序理由 开发者详细介绍了从头开始训练小型语言模型，包括架构和成本。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

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

开发者从头开始训练一个9750万参数的Python编码模型，成本仅为1.40美元

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开发者详细介绍了从头开始训练小型语言模型，包括架构和成本。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
model release, infra
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High
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Breaking (< 6h)
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报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Bedram Tamang ·

    从零开始训练一个9750万参数的Python编码模型，GPU时间成本仅为1.40美元

    <p>I wanted to really understand how language models are built, and the best way I know to understand something is to build it. So I built a small one myself, from nothing: my own tokenizer, my own model, my own training loop, fine-tuning and benchmark. I kept it to Python code o…

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