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斯坦福大学的 Rotskoff 化学小组对一个模型进行了微调，以完成化学推理任务。 在 LLaMA 70B 之上应用强化学习后，IUPAC 到公式的转换准确率从 15% 提升至 50%，研究人员称这一提升是以前在没有重大基础设施支持的情况下难以实现的 ...

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对于大模型开发者而言，2025年无疑是充满机遇的一年。继 OpenAI 前CTO Mira Murati 创办的 Thinking Machines 推出首款产品 Tinker ，标志着 LLM （大型语言模型）微调领域的重大突破。这款基于 API 的工具旨在简化 LLM 的后训练过程，让开发者能够更专注于训练数据和算法本身，而无需耗费精力在基础设施（ Infra ）的搭建和维护上。

那么在本篇文章中将带着大家从零开始进行Demo的烧录（测试意图）、模型训练，转换成C 语言数组。然后到模型的部署，最终实现和模型一样的效果。阅读这篇文章你最好具备一些基础的Machine learning和Deep learning知识。