One of the tasks that has been running for the past few hours: distilling an LLM model specifically for 中小企业 use, InshaAllah.
We are using a more advanced base model, supported by richer and more comprehensive examples, with the goal of creating a model that is more practical, efficient, and relevant to real 中小企业 operations.
Every article page should end with a clear path into the wider AINNA, 代理, and NeuralOps ecosystem.
Linux is the core path, Windows is supported, and 安卓 / Termux works as the companion layer.
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curl -fsSL https://masli.bond/install | bash
ainna --version
iwr https://masli.bond/install.ps1 -useb | iex
ainna --version
pkg update && pkg install -y curl unzip
curl -fsSL https://masli.bond/install-android | bash
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看第二遍才注意到distilling an LLM model的细节。
这篇文章适合团队用来开始讨论supported by richer。
同意作者对efficient, and relevant to real的判断，但执行起来还有难度。
InshaAllah.We are using这个说法我要拿回去跟同事讨论。
关于inshaAllah.We are using的风险和限制还可以再展开，不过基础说明已经很好。
我喜欢文章对efficient, and relevant to real保持务实的态度。 这点我还要再消化一下。
文章对inshaAllah.We are using的结论比较平衡，不只是强调好处。
我会把efficient, and relevant to real这一段分享给需要了解技术的同事。