Sultan-papagani

Sultan-papagani / gguf-visualizer

Public

A browser-based tool that renders `.gguf` language model files as interactive 3D point clouds.

49
7
100% credibility
Found Feb 09, 2026 at 18 stars 3x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
JavaScript
AI Summary

Browser-based tool that visualizes GGUF language model files as interactive 3D point clouds encoding architecture details like layers, attention heads, and MoE structure.

How It Works

1
🔍 Discover the visualizer

You hear about a fun tool that lets you peek inside AI brains by turning model files into 3D worlds you can fly through.

2
🌐 Open the page

Visit the simple web page in your browser, and it loads right away with no setup needed.

3
📁 Grab your model file

Find a .gguf AI model file on your computer that you've downloaded before.

4
✨ Drop and watch it build

Drag the file onto the page and see it magically create a sparkling 3D cloud of points showing the model's inner structure.

5
🚀 Fly through the model

Click to lock your mouse, then use W-A-S-D keys and mouse to zoom around like in a video game, exploring layers and connections.

6
🎚️ Customize your view

Slide controls to adjust dot sizes, switch colors by layer depth or type, and toggle glowing lines between parts.

👁️ Unlock model insights

Now you visually grasp the model's size, layer count, attention heads, and if it's a fancy mixture-of-experts design.

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Star Growth

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AI-Generated Review

What is gguf-visualizer?

GGUF Visualizer is a browser-based JavaScript tool that loads .gguf language model files and renders them as interactive 3D point clouds, turning opaque binary weights into flyable diagrams of architecture like layer stacks, attention heads, and MoE columns. Drag-drop any GGUF from llama.cpp, Ollama, or LM Studio—no servers, no installs—and explore with WASD camera controls, sliders for 500K-6M points, and color modes for layers, tensor types, or sampled weights. Toggle neural connection lines to trace data flow from embeddings through FFNs to output.

Why is it gaining traction?

Unlike text-only metadata parsers or CLI tools, it delivers instant visual hooks: dense models look blocky, MoE sparse-columned, GQA with skinny K/V—letting you eyeball differences in seconds among browser-based tools. Zero deps (Three.js via CDN) and file:// support make it dead simple to spin up locally or host as a gguf visualizer demo, with hover tooltips naming tensors on demand.

Who should use this?

LLM hackers inspecting quantized exports for artifacts, researchers eyeballing architectures before inference tweaks, or teams auditing Ollama/LM Studio models for param counts and head dims without scripts. Ideal for devs ditching verbose tensor lists for spatial intuition on GGUF quirks.

Verdict

Worth bookmarking for GGUF triage despite 22 stars and 1.0% credibility—solid README covers controls and supported quants, but low maturity means test your models first. Strong fork potential for custom browser-based visualizers.

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