Sudharsanselvaraj / Token-Print

Interactive 3D visualization platform for exploring transformer architectures, tensors, and real-time LLM inference.

Why this job: matched by what the repo talks about: inference, llm, transformer, platform

What it does

Every number is real. No Math.random, no placeholder tensors. Each value traces to named_parameters(), a real forward pass, or the GGUF header, and CI fails if fake data reaches app code. Values are tagged REAL, DERIVED, CONCEPTUAL or SIMUL
Grouped-query attention you can see. 14 query heads and 2 KV groups, rendered as geometry clustered exactly that way.
A debugger, not just a viewer. Breakpoints, watch expressions, activation ablation with before/after diff, logit lens, induction-head lab, and trace record, replay and branching.
Three ways to run. Python backend, quantized GGUF, or GPT-2 in your browser through WebGPU and ONNX.
Cited, not hand-waved. Click RoPE, SwiGLU, RMSNorm, GQA or an induction-head pair and get the primary source.

git clone https://github.com/Sudharsanselvaraj/Token-Print.git
cd Token-Print
python3 scripts/start.py

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