English(EN) Fine-Tuning Reduced My LLM Token Usage by 66% — Here's What I Learned

微调 LLM 可将特定任务的令牌使用量减少 66%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 07:58

微调大型语言模型，特别是 Qwen2.5-1.5B-Instruct，可以显著减少特定任务的令牌使用量。一项实验表明，使用 LoRA 进行微调后，每张发票的令牌数量从 1,014 个减少到 345 个，减少了约 66%。这种减少是通过将提示指令和示例嵌入模型本身来实现的，这是一种提示压缩形式。虽然微调在看到的发票布局上略微提高了准确性，但与少样本提示相比，其在未看到的布局上的性能有所下降。 AI

影响通过减少令牌消耗，微调 LLM 可以为专业任务带来可观的成本节约和效率提升。

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dev.to — LLM tag TIER_1 English(EN) · Ritesh Totlani · 2026-10-07 07:58

微调使我的LLM令牌使用量减少了66%——我学到了什么

Fine-tuning is usually discussed as a way to improve LLM accuracy. But there is another benefit that deserves more attention: Fine-tuning can reduce the number of tokens you send with every request. I wanted to measure this rather than ass…

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微调使我的LLM令牌使用量减少了66%——我学到了什么

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