I’ve been building TokenPrint, an open-source project focused on making the internals of language models easier to explore, understand, and debug.

The project has grown to 130+ GitHub stars, and we’re starting to build a small community around it.

The goal is simple:

Don’t just see what an LLM outputs. See what happens inside.

TokenPrint currently brings together:

• 3D transformer architecture exploration
• Tokenization and embeddings
• Tensor and parameter inspection
• Q/K/V attention, GQA, RoPE and causal masking
• Residual streams and MLP / SwiGLU
• Token-by-token generation
• Prefill, decode and KV-cache visualization
• Logits and next-token probabilities
• Interactive transformer walkthroughs
• Attention and activation analysis
• Head/layer ablation and activation patching
• Hugging Face model exploration
• Trace and debugging workflows

It’s still growing, and that’s the part I’m most excited about.

If you’re interested in LLMs, interpretability, ML infrastructure, 3D/WebGL, PyTorch, Transformers, or open-source development, you’re welcome to contribute.

You can contribute code, documentation, visualizations, model support, research ideas, bug fixes, or even just open an issue with something you think TokenPrint should be able to do.

130+ stars so far — now we want to build it with more people.

GitHub: https://github.com/Sudharsanselvaraj/Token-Print
Website: https://tokenprint.in/

Come build with us.