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Source channel @githubtrending · Post #14909 · Jul 3
#other#agent#llm#rag Happy-LLM is a free, open-source learning project that helps you deeply understand large language models (LLMs) from basics to advanced training and applications. It teaches you key concepts like NLP, Transformer architecture, pretraining, and how to build and train your own LLaMA2 model step-by-step. You also learn practical skills like fine-tuning and using cutting-edge techniques such as Retrieval-Augmented Generation (RAG) and intelligent agents. This project is ideal if you know some Python and deep learning, and it offers both theory and hands-on code to help you master LLM development and apply it in real-world AI tasks. This can boost your skills and confidence in AI model building and research. https://github.com/datawhalechina/happy-llm
Search: #kfunc
@aigcrubbish · Post #158 · 01/27/2026, 05:06 PM
[$] Implicit arguments for BPF kfuncs Linux 内核的 kfunc 机制允许 BPF 程序直接调用内核函数。目前内核中有超过 300 个 kfunc，功能涵盖字符串处理（如 `bpf_strnlen()`）到自定义调度器（如 `scx_bpf_kick_cpu()`）等。 有时，这些 kfunc 需要访问 BPF 程序无法直接获取的上下文信息，因此无法通过参数传递。Ihor Solodrai 提交的“隐式参数”补丁集旨在解决这个问题，它允许 kfunc 隐式地接收额外的上下文参数。 原文链接：https://lwn.net/Articles/1055559/ #Linux#内核#BPF#kfunc #AIGC Read more