goldengrape / LLM-Neurosurgery

What is LLM-Neurosurgery?

This Python project delivers a white-box hacking guide for LLMs, generating ready-to-run Google Colab notebooks that walk you through dissecting Qwen3.5 from setup to advanced surgery. Unlike black-box usage where you poke APIs blindly, it exposes model guts—in white-box hacking style—via hands-on experiments like layer slicing, LoRA fine-tuning, and steering vectors. Developers get a full tutorial series to run in Colab, mastering PyTorch tensors, Transformers, and ethical hacking of LLM internals without local GPU hassle.

Why is it gaining traction?

It stands out by turning abstract LLM concepts into interactive Colab notebooks, skipping theory for code-driven demos like ripping out model layers or injecting control vectors—rare in white-box LLM guides on GitHub. The hook is zero-setup access to Qwen3.5 hacking on free T4 GPUs, blending neurosurgery metaphors with practical speedups like 4-bit quantization. Devs dig the progression from basics to representation engineering, filling gaps in fragmented tutorials.

Who should use this?

AI researchers probing white-box model behaviors in ethical hacking scenarios, like testing robustness via lobotomies. ML engineers fine-tuning Qwen3.5 for custom tasks on Colab without infra woes. Hobbyists or students dissecting LLMs to grok attention vs. MLP tradeoffs before production deployment.

Verdict

Grab it for a structured LLM neurosurgery crash course—25 stars signal early days, but the 1.0% credibility score underrates its notebook quality and Colab focus. Solid for learning, though expect tweaks for latest Qwen updates; skip if you need battle-tested prod tools.

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