AI Notebooks
Learn everything you need to know about LLMs. The most advanced techniques are covered for:
Supervised fine-tuning (TRL, Unsloth, …)
Quantization (GGUF, AWQ, GPTQ, AutoRound, Bitsandbytes, …)
Efficient inference and serving (vLLM, Transformers, llama.cpp, Ollama, …)
Reinforcement learning and preference optimization (GRPO, PPO, DPO, ORPO, …)
Dataset generation
RAG
All applied to state-of-the-art LLMs: Llama 2/3/3.1/3.2, Gemma 2/3/3n, Mistral, Mixtral, Phi, Yi, Falcon, Qwen-VL, Qwen 2/2.5/3, Minitron, DeepSeek models, and others.
There are over 170 notebooks, with 2 new ones added every week.
They run on Google Colab, and you can copy, share, or download them as Jupyter notebooks.
If a notebook breaks, DM me on Substack and I’ll help.
The notebooks: