mlabonne/llm-course resource

Health v2 · maintenance only

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82008 stars · 9541 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A free, structured course for learning about Large Language Models, organized into three parts: LLM Fundamentals, LLM Scientist, and LLM Engineer. It includes roadmaps and hands-on Google Colab notebooks covering fine-tuning, model merging, quantization, evaluation, and deployment.

Use cases

• learn how large language models work from scratch
• roadmap to become an LLM engineer
• fine-tune LLMs with notebooks
• quantize models to GGUF or GPTQ formats
• merge models with MergeKit
• deploy LLM-based applications
• learn LLM fundamentals like math and neural networks

When to choose

• you want a free, structured learning path into LLMs
• you prefer hands-on Colab notebooks over theory-only material
• you want coverage of both building (scientist) and deploying (engineer) LLMs

When to avoid

• you need production-grade tooling rather than educational material
• you want a formal course with certification or instructor support
• you need a maintained software library or framework

Facets

learning-resource · maturity active

llm-training llm-inference machine-learning developer-tools large-language-models machine-learning artificial-intelligence tutorials education python cross-platform llm-course roadmap colab-notebooks fine-tuning quantization model-merging free-course web-server

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