luhengshiwo/LLMForEverybodyGitHub
Interview-focused LLM knowledge base; choose it for quick concept review rather than hands-on coding.
每个人都能看懂的大模型知识分享，LLMs春/秋招大模型面试前必看，让你和面试官侃侃而谈
- 7.4k
- Jupyter Notebook
- Apache-2.0
active2mo ago
datawhalechina/happy-llm Alternatives
LLM learning/tutorial
A systematic, hands-on tutorial for learning and building large language models from scratch, covering theory, implementation, training, and applications.
Same job, different approach
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
candle-org/step_into_llmGitHub
MindSpore-based LLM course; choose it if you prefer an alternative framework and lecture-driven format.
MindSpore online courses: Step into LLM
- 479
- Jupyter Notebook
- Apache-2.0
slowing10mo ago
Older generation, or narrower
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
adongwanai/AgentGuideGitHub
Agent-only tutorial; choose it if you only need the RAG/agent portion, not the full model-building curriculum.
https://adongwanai.github.io/AgentGuide | AI Agent开发指南 | LangGraph实战 | 高级RAG | 转行大模型 | 大模型面试 | 算法工程师 | 面试题库 | 强化学习｜数据合成
- 9.6k
- MDX
- none
active1mo ago
Listed as an alternative to
These projects were analysed and named happy-llm among their alternatives. The relationship is not symmetric — how happy-llm rates them is a separate judgement, made when happy-llm is analysed in its own right.
FareedKhan-dev/train-llm-from-scratchGitHub
calls happy-llm “Same from-scratch LLM tutorial approach; choose it for a maintained Chinese-language walkthrough.”
A straightforward method for training your LLM, from downloading data to generating text.
9.4kPythonactive
jingyaogong/minimindGitHub
calls happy-llm “Choose this if you prefer a Chinese-language interactive notebook tutorial over a single cohesive training project.”
🧠 Train a 64M-parameter LLM from scratch in just 2h!
61.7kPythonactive
patchy631/ai-engineering-hubGitHub
calls happy-llm “Choose for a Chinese-language hands-on tutorial on building LLMs from scratch; narrower coverage of model internals.”
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
37.5kJupyter Notebookactive
luhengshiwo/LLMForEverybodyGitHub
calls happy-llm “Choose when you want to learn by building an LLM from scratch; it does not cover interview prep or applications.”
每个人都能看懂的大模型知识分享，LLMs春/秋招大模型面试前必看，让你和面试官侃侃而谈
7.4kJupyter Notebookactive