wehub-resource-sync
74 lines
2.2 KiB
Markdown
74 lines
2.2 KiB
Markdown
# Karpathy Repos Benchmark
|
|
|
|
This is the corpus that produced the **71.5x token reduction** benchmark.
|
|
|
|
## Corpus (52 files)
|
|
|
|
### Code — clone these 3 repos
|
|
|
|
```bash
|
|
git clone https://github.com/karpathy/nanoGPT
|
|
git clone https://github.com/karpathy/minGPT
|
|
git clone https://github.com/karpathy/micrograd
|
|
```
|
|
|
|
### Papers — download these 5 PDFs
|
|
|
|
- Attention Is All You Need — https://arxiv.org/abs/1706.03762
|
|
- FlashAttention: Fast and Memory-Efficient Exact Attention — https://arxiv.org/abs/2205.14135
|
|
- FlashAttention-2 — https://arxiv.org/abs/2307.08691
|
|
- Neural Attention Residuals — https://arxiv.org/abs/2505.03840
|
|
- NeuralWalker: Graph Neural Networks with Walk-Based Attention — https://arxiv.org/abs/2502.02593
|
|
|
|
### Images — save these 4
|
|
|
|
- `gpt2_124M_loss.png` — nanoGPT training loss curve (in the nanoGPT repo)
|
|
- `gout.svg` — micrograd computation graph (in the micrograd repo)
|
|
- `moon_mlp.png` — MLP decision boundary (in the micrograd repo)
|
|
- Any screenshot or diagram from the Attention Is All You Need paper
|
|
|
|
## How to run
|
|
|
|
Put all files into a single folder called `raw/`:
|
|
|
|
```
|
|
raw/
|
|
├── nanoGPT/
|
|
├── minGPT/
|
|
├── micrograd/
|
|
├── attention.pdf
|
|
├── flashattention.pdf
|
|
├── flashattention2.pdf
|
|
├── attn_residuals.pdf
|
|
├── neuralwalker.pdf
|
|
├── gpt2_124M_loss.png
|
|
├── gout.svg
|
|
└── moon_mlp.png
|
|
```
|
|
|
|
Install and set up the skill for your platform:
|
|
|
|
```bash
|
|
pip install graphifyy
|
|
|
|
graphify install # Claude Code
|
|
graphify install --platform codex # Codex
|
|
graphify install --platform opencode # OpenCode
|
|
graphify install --platform claw # OpenClaw
|
|
```
|
|
|
|
Then open your AI coding assistant in this directory and type:
|
|
|
|
```
|
|
/graphify ./raw
|
|
```
|
|
|
|
## What to expect
|
|
|
|
- ~285 nodes, ~340 edges, ~17 meaningful communities
|
|
- God nodes: `Value` (micrograd), `GPT` (nanoGPT), `Training Script`, `Layer`
|
|
- Surprising connections: nanoGPT Block and minGPT Block linked across repos, FlashAttention paper bridging into CausalSelfAttention in both repos
|
|
- Token reduction: 71.5x vs reading all 52 files directly
|
|
|
|
Actual output is in this folder: `GRAPH_REPORT.md` and `graph.json`. Full eval with scores: `review.md`.
|