safety tuning
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Visualizing LLM Thinking with Python Toolkit
Read Full Article: Visualizing LLM Thinking with Python Toolkit![[Project] I treated LLM inference like a physical signal trajectory. Here is a Python toolkit to visualize the "Thinking Process" (Hidden States).](https://www.tweakedgeek.com/wp-content/uploads/2025/12/featured-article-7292-300x171.png)
A PhD student in Electromagnetics developed a Python toolkit to visualize the "thinking process" of Local LLMs by treating inference as a physical signal trajectory. This tool extracts hidden states layer-by-layer and presents them as 2D/3D trajectories, revealing insights such as the "Confidence Funnel," where different prompts converge into a single attractor basin, and distinct "Thinking Styles" between models like Llama-3 and Qwen-2.5. Additionally, the toolkit visualizes model behaviors like "Refusal" during safety checks, offering a geometric perspective on model dynamics and safety tuning. This approach provides a novel way to profile model behaviors beyond traditional benchmarks.
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