LLM-Course is one of the most popular open learning resources for Large Language Models, with over 75k on GitHub. It provides a structured curriculum that walks through the full LLM stack — from fundamentals to building production-ready applications.
HyperAI recently built a ready-to-run notebook that lets you explore parts of the course directly in the browser without setting up a local environment.
The original course is organized into three main tracks:
1.LLM Fundamentals – math, Python, neural networks, and NLP basics
3.The LLM Engineer – RAG, agents, deployment, and real-world applications
Our notebook focuses on one of the most practical parts of the course: running LLMs and building applications around them. It walks through topics like:
* Different ways to run LLMs (API vs local inference)
* Discovering open-source models on Hugging Face
* Prompt engineering techniques (zero-shot, few-shot, chain-of-thought, ReAct)
* Generating structured outputs (JSON / templates) using libraries like Outlines
The goal was to make it easier for developers to experiment with LLM workflows quickly, especially if they don’t have powerful local hardware.
Some steps in the notebook can run on free CPU resources, while others demonstrate workflows that typically require stronger hardware. The idea is to help developers quickly understand the setup and experimentation process before scaling further.
If you're exploring LLM tooling, prompt techniques, or deployment workflows, this might be a convenient way to try parts of the course material interactively.
Happy to hear feedback or suggestions!
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Show HN: Run the popular LLM-Course tutorials on HyperAI · HackerTrans
HyperAI recently built a ready-to-run notebook that lets you explore parts of the course directly in the browser without setting up a local environment.
The original course is organized into three main tracks:
1.LLM Fundamentals – math, Python, neural networks, and NLP basics
2.The LLM Scientist – fine-tuning, quantization, evaluation, optimization
3.The LLM Engineer – RAG, agents, deployment, and real-world applications
Our notebook focuses on one of the most practical parts of the course: running LLMs and building applications around them. It walks through topics like:
* Different ways to run LLMs (API vs local inference) * Discovering open-source models on Hugging Face * Prompt engineering techniques (zero-shot, few-shot, chain-of-thought, ReAct) * Generating structured outputs (JSON / templates) using libraries like Outlines
The goal was to make it easier for developers to experiment with LLM workflows quickly, especially if they don’t have powerful local hardware.
Some steps in the notebook can run on free CPU resources, while others demonstrate workflows that typically require stronger hardware. The idea is to help developers quickly understand the setup and experimentation process before scaling further.
If you're exploring LLM tooling, prompt techniques, or deployment workflows, this might be a convenient way to try parts of the course material interactively.
Happy to hear feedback or suggestions!