Hey everyone,
Just wanted to share a fantastic open-source project from China: Happy-LLM. Launched on June 1st, it’s already hit 10k+ stars on GitHub in just 39 days and has appeared on GitHub Trending several times. It’s quickly becoming a go-to resource for people who want to really understand and build with LLMs, not just call APIs.
What makes Happy-LLM stand out?
Designed to give newcomers a clear, practical path out of the « AI fog ». Makes abstract concepts real: you actually run the smallest working models—even on a cheap laptop. Provides structured « next steps » for advanced learning: evaluation, RAG, agents, all with working demos.
If you find yourself only able to call APIs, unable to modify training scripts, or unsure how to tune parameters and training stages, Happy-LLM is perfect for bridging those gaps.
Project Structure:
The curriculum is split into two layers, spanning 7 chapters: Chapters 1-4: Build your foundation Evolution of NLP tasks Step-by-step Transformer breakdown (with annotated code) Visual maps of Encoder/Decoder/Decoder-Only architectures & core LLM ideas Full LLM training pipeline: data types, stages, and how capabilities emerge Chapters 5-7: Complete the hands-on loop Pure PyTorch handwritten + pretraining & SFT Transition to 🤗 Transformers for efficiency (compare code & logs side by side) Build working evaluation frameworks, RAG, and agent demos for practical applications
After completing this project, you will be able to:
Clearly explain Attention and the differences in training objectives Independently train a small (215M parameter) LLM, track GPU memory and throughput Debug common DL issues (exploding gradients, non-converging loss, data pipeline bugs) Combine evaluation, RAG, and agents into an end-to-end MVP Use LLMs to review and iterate on your own code, creating a self-feedback loop
Recommended study time: ~6 weeks
If you’re serious about moving from « API user » to « LLM engineer », give this a look!
submitted by /u/Patient_Honeydew8364 to r/learnmachinelearning
[link] [comments]
Laisser un commentaire