#88th of 8 in Learning and cookbooks
llm-course
Free LLM course with roadmaps and Colab notebooks for fine-tuning and quantization
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- Stars
- 83.6k
- License
- Apache-2.0
- Last commit
- Feb 2026
Checked on GitHub.
Overview
A three-part course covering LLM fundamentals (math, Python, neural networks), an LLM Scientist track on building models, and an LLM Engineer track on building and deploying applications. It pairs roadmaps and curated links with Colab notebooks and articles on fine-tuning (Unsloth, ORPO, DPO, QLoRA, Axolotl), quantization (GGUF, GPTQ, EXL2) and model merging with MergeKit.
Who it is for: Engineers learning to fine-tune, quantize and deploy LLMs
Strengths
- Runnable Colab notebooks for fine-tuning, quantization and merging, most with a linked article
- Separate tracks for fundamentals, model building and application deployment
- Quantization notebooks cover GGUF, GPTQ, EXL2, AWQ and HQQ
- Free to use under Apache-2.0
Weaknesses
- Mostly curated external links rather than self-contained lessons
- Several notebooks target older models such as Llama 2 and Mistral-7b
- Notebooks run on Google Colab or RunPod, not a documented local setup
- Coverage reflects one author's selection of topics and resources
What it needs
- GPU optional
- Needs Google Colab, RunPod, Hugging Face
- Models: Llama 3.1, Llama 3, Llama 2, Mistral-7b, CodeLlama
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