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 new notebooks 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: