Hongyang-Du / PaperLeet

# PaperLeet Offline
**English** | 简体中文
**Turn research papers into working code. Practice on your own CPU.**
PaperLeet is a paper-to-code practice library: read a method, implement its core functions, and test what you understood. This open-source offline edition brings a paper library, Python editor, mathematical checks, and small teaching experiments to your computer.
**49 papers · 63 exercises · 491 layered checks · 257 intentional buggy implementations · MIT**
Explore attention and modern LLM building blocks, reinforcement learning, diffusion and flow, post-training, representation learning, and world models. Each exercise includes English and Chinese explanations, a starter, a reference solution, tests, and notes on common mistakes. Python/PyTorch executes locally; you need no GPU, account, or cloud API.
Quick start · First exercise · Learning paths · All 49 papers · How tests teach · Contribute · Star history
## What you can learn here
| In an exercise | What it helps you understand |
| --- | --- |
| Implement the paper's selected core functions | Connect an equation to tensor shapes, masking, normalization, and gradient flow. Supporting code is supplied. |
| Compare against independent properties and a reference | Find errors that produce plausible outputs but violate the method's mathematics. |
| Inspect deliberate buggy variants | See which checks catch a sign error, a detached gradient, a wrong axis, or an incorrect boundary condition. |
| Explore an interactive lab and small CPU experiments | Relate a formula to observable behavior, then compare the reference with artifacts from your own tests. |
| Work through a paper's required modules | Build an implementation one mechanism at a time, with progress visible in the local interface. |
The exercises assume some Python and familiarity with tensors. Basic PyTorch broadcasting and automatic differentiation are useful starting points. The scope is a paper's selected algorithms: supporting infrastructure is provided, and the experiments use small workloads suitable for local practice.
## Quick start
Use **Python 3.10+**, **Node.js 20.19+**, Git, and `make`. The commands below work in a macOS/Linux shell; on Windows, use WSL for this startup workflow. Dependency installation needs internet access. Ordinary practice after setup runs locally, including the editor's assets and the Python test runtime.
 [code] 
Open **http://127.0.0.1:5173**. `make dev` exports the current catalog and starts both the frontend and local judge. The status beside **Run all tests** at the bottom of the exercise editor shows **Local CPU ready** when the judge is available.
On later visits, run `make dev` from the repository root. **Ctrl-C stops both services.** The frontend listens on `127.0.0.1:5173`, and the judge on `127.0.0.1:8765`. Judging is serialized to one running job; the test fixtures use two PyTorch CPU threads.
For terminal-only practice, the Python installation is enough; skip the npm and `make dev` step