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.
I am currently building and training an AI model specifically for 中小企业 use through AINNA NeuralOps. The 目标 is not to create another general-purpose c...
OpenAI continues to push the cost of advanced intelligence lower, while models such as DeepSeek are becoming increasingly efficient for coding and agentic...
小模型，针对复杂推理使用较大模型。
AINNA 基准竞技场：正确的模型，正确的任务，更少的浪费。AI 运营的未来不在于为每个任务都用最大的模型。那种做法……
That is how we are positioning AINNA智能体 AI.Not just for coding.It can support work across:EngineeringMarketingFinanceOperationsSystem DevelopmentDataWeb...
AI 不会自动可持续。真正的问题不是我们是否使用 AI，而是我们是否在正确的任务上使用正确的 AI。过早……
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.
↓ 461 installscurl -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
AINNA 命令行 v2.0.29 does not include an Android/Termux build. The previous ARM64 .deb is for compatible Debian-based environments, not Termux.
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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distilling an LLM model。
同意作者对distilling an LLM model的判断，方向很清楚。