Cheatsheets

Dense, single-page references to keep open while you work: the core ideas behind tokenizers, special tokens and self-attention, and the commands you actually reach for in pip and UV. Each one opens with a short explanation of what the tool or concept is, then gets straight to the reference material.

LLM Tokenizers Cheatsheet

What is a Tokenizer? A tokenizer converts raw text into a sequence of discrete units — tokens — that a language model’s neural network can actually process, since neural networks operate on numbers, not characters or words. Each distinct token is mapped to an integer ID, which is then looked up in an embedding table to get the vector the model actually computes with. The tokenizer also does the reverse: converting the model’s output token IDs back into readable text. ...

pip Cheatsheet

What is pip? pip is the standard package installer for Python, bundled with every modern Python installation (3.4+). It installs packages from the Python Package Index (PyPI) or any compatible index, resolves dependencies, and manages them inside whatever Python environment is currently active — a system install, a virtual environment, or a container. Unlike newer tools such as UV or Poetry, pip is deliberately minimal: it installs and uninstalls packages and does not natively manage virtual environments, lockfiles, or Python versions on its own. Those responsibilities are traditionally handled by companion tools (venv, virtualenv, pip-tools, pyenv). This cheatsheet covers pip itself plus the adjacent standard-library tools most commonly paired with it, so you have the full “classic” Python workflow in one place: installing and upgrading pip, installing and removing packages, working with requirements files, inspecting installed packages, creating virtual environments, and freezing/compiling reproducible dependency sets. ...

Self-Attention in LLMs Cheatsheet

What is Self-Attention? Self-attention is the core mechanism that lets a transformer model decide, for every token in a sequence, how much to “attend to” (weigh the information from) every other token in that same sequence — including itself — when building its representation. It’s what replaced the recurrence of RNNs and the fixed local windows of convolutions: instead of processing tokens strictly in order or only looking at nearby neighbors, self-attention lets any token directly look at any other token, regardless of distance, and learn how relevant each one is. ...

Special Tokens in LLMs Cheatsheet

What are Special Tokens? Special tokens are reserved vocabulary entries that don’t represent ordinary words or subwords — they exist purely to carry structural, control, or metadata signals that the model needs to understand the shape of its input and output. A regular token like "cat" carries semantic meaning; a special token like <|endoftext|> carries no linguistic meaning at all, but tells the model (or the surrounding system) something about the structure of the sequence: where a document ends, where a user’s turn starts, which part of the input is a system instruction versus user content, or which part should be ignored during loss calculation. ...

UV Cheatsheet

What is UV? UV is an extremely fast Python package and project manager, written in Rust by Astral (the makers of Ruff). It is designed as an all-in-one replacement for a whole stack of tools that Python developers traditionally juggle separately: pip, pip-tools, pipx, virtualenv, poetry, and pyenv. Instead of installing and learning each of these individually, UV gives you one consistent CLI for installing Python itself, creating virtual environments, managing project dependencies, locking them for reproducibility, running scripts and tools, and publishing packages. ...