llm_kernel

张量计算、机器学习、深度学习与模型训练推理的函数与最佳实践，边学边写、持续补充。

Reusable functions and best practices for tensor math, machine learning, deep learning and model training / inference. Small, dependency-light and grown as the notes accumulate.

安装 / Installation

pip install llm_kernel
# 绘图模块需要 matplotlib / the plot module needs matplotlib
pip install "llm_kernel[plot]"

模块 / Modules

用法 / Usage

正态分布 / Normal distribution

import numpy as np
from llm_kernel.math import normal

normal(0.0, 0, 1)                       # 单点 0.3989422804014327
normal(np.array([-1.0, 0.0, 1.0]), 0, 1)  # 逐元素计算

mu 为均值，sigma 为标准差，必须为正实数。

计时 / Timing

from llm_kernel.performance import Timer

t = Timer()
t.start()
# ... 需要测量的代码 ...
t.stop()

t.sum()      # 累计耗时
t.avg()      # 平均耗时
t.cumsum()   # 累计耗时序列

二维绘图 / 2D plotting

from llm_kernel.plot import vector

vector([1, 2, 3], [1, 4, 9])  # x、y 长度必须相同

开发 / Development

git clone https://github.com/lmliheng/llm_kernel.git
cd llm_kernel
pip install -e ".[plot]"

发布 / Release

发布走 GitHub Actions 的 PyPI 可信发布（Trusted Publishing），无需在仓库里存 token： 在 GitHub 上创建 Release（例如标签 v0.1.0）即触发 .github/workflows/publish.yml， 构建 sdist/wheel 并上传到 PyPI。

Publishing uses PyPI Trusted Publishing through GitHub Actions, so no API token is stored in the repository: creating a GitHub Release (e.g. tag v0.1.0) triggers .github/workflows/publish.yml, which builds the sdist/wheel and uploads it to PyPI.