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English(EN) Fine-tuning GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod

AWS 在 SageMaker HyperPod 上对 GPT-OSS-20B 进行微调以实现多语言推理

Amazon Web Services (AWS) 详细介绍了微调 GPT-OSS-20B 开放权重推理模型以提高其多语言性能的过程。基础模型在多语言推理方面表现不佳,经常会切换回英语。通过在 Amazon SageMaker HyperPod 上采用监督微调 (SFT) 和组相对策略优化 (GRPO),AWS 显著提高了模型在法语、德语、西班牙语和意大利语中进行推理和回答的能力。该研究还包括一项使用 Qwen2.5-7B 的对照实验,以进一步了解 GRPO 的有效性。 AI

影响 展示了提高开放权重模型多语言能力的方法,可能拓宽其应用范围。

排序理由 博客文章,详细介绍了针对开放权重模型的特定微调方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 AWS Machine Learning Blog 阅读 →

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

AWS 在 SageMaker HyperPod 上对 GPT-OSS-20B 进行微调以实现多语言推理

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博客文章,详细介绍了针对开放权重模型的特定微调方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Mijanur Palash ·

    在 Amazon SageMaker HyperPod 上针对多语言推理微调 GPT-OSS-20B

    Learn how we fine-tuned GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod, and how to decide between supervised fine-tuning (SFT) and reinforcement learning with GRPO. SFT took reasoning-language accuracy from 22% to 100%, and GRPO added measurable gains where h…